Callout Paper Output
J.D. Raffa
Sept 5, 2018
Just to review:
- We decided to only use MICU patients.
- Patients had to link to Census data, which eliminated some of the older ICU stays (pre 2005, and others).
- Excluded 2012 patients, as the quality is suspect.
- Hour of Callout, Hour of Discharge, and Discharge Delay are all related in a somewhat complex fashion.
## [1] 27288## [1] 27221## [1] 11038## [1] 11038## [1] 9673Below are the Overall patient characteristics.
| variable_name | level | Overall |
|---|---|---|
| n | 9673 | |
| micu (%) | 1 | 9673 (100.0) |
| age (mean (sd)) | 63.68 (17.94) | |
| callout_month (%) | 1 | 774 ( 8.0) |
| 2 | 738 ( 7.6) | |
| 3 | 733 ( 7.6) | |
| 4 | 726 ( 7.5) | |
| 5 | 794 ( 8.2) | |
| 6 | 745 ( 7.7) | |
| 7 | 779 ( 8.1) | |
| 8 | 877 ( 9.1) | |
| 9 | 851 ( 8.8) | |
| 10 | 914 ( 9.4) | |
| 11 | 823 ( 8.5) | |
| 12 | 919 ( 9.5) | |
| female (%) | 0 | 4956 ( 51.2) |
| 1 | 4717 ( 48.8) | |
| request_tele (%) | 0 | 6318 ( 65.3) |
| 1 | 3355 ( 34.7) | |
| request_resp (%) | 0 | 9514 ( 98.4) |
| 1 | 159 ( 1.6) | |
| request_cdiff (%) | 0 | 9190 ( 95.0) |
| 1 | 483 ( 5.0) | |
| request_mrsa (%) | 0 | 8405 ( 86.9) |
| 1 | 1268 ( 13.1) | |
| request_vre (%) | 0 | 9182 ( 94.9) |
| 1 | 491 ( 5.1) | |
| oasis (mean (sd)) | 29.84 (7.30) | |
| elixhauser_hospital (mean (sd)) | 3.54 (7.28) | |
| ethnicity (%) | White | 6910 ( 71.4) |
| African American/Black | 1494 ( 15.4) | |
| Other | 1269 ( 13.1) | |
| MED_SERVICE (%) | FALSE | 692 ( 7.2) |
| TRUE | 8981 ( 92.8) | |
| HOSP_FREE_DAYS (median [IQR]) | 24.12 [20.29, 26.02] | |
| callout_dayofweek (%) | friday | 1442 ( 14.9) |
| monday | 1300 ( 13.4) | |
| saturday | 1292 ( 13.4) | |
| sunday | 1267 ( 13.1) | |
| thursday | 1412 ( 14.6) | |
| tuesday | 1422 ( 14.7) | |
| wednesday | 1538 ( 15.9) | |
| CALLOUT_DURING_NIGHT (%) | FALSE | 9607 ( 99.3) |
| TRUE | 66 ( 0.7) | |
| CALLOUT_DURING_ROUNDS (%) | FALSE | 3816 ( 39.5) |
| TRUE | 5857 ( 60.5) | |
| DISCHARGEDELAY_HOURS (mean (sd)) | 10.37 (10.28) | |
| hourofcallout2 (median [IQR]) | 11.40 [10.12, 13.17] | |
| PROPFULL_BEDS (mean (sd)) | 0.91 (0.09) | |
| postcalldaycat2 (%) | 0 | 7800 ( 80.6) |
| [1,5] | 1873 ( 19.4) | |
| hospitaldeath (%) | 0 | 9140 ( 94.5) |
| 1 | 533 ( 5.5) | |
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.15] | |
| los_post_callout_days (median [IQR]) | 4.16 [2.28, 7.24] | |
| los_post_icu_days (median [IQR]) | 3.80 [1.95, 6.89] | |
| los_pre_callout_days (median [IQR]) | 1.77 [0.94, 3.68] | |
| callout_year (%) | 2005 | 376 ( 3.9) |
| 2006 | 1070 ( 11.1) | |
| 2007 | 1417 ( 14.6) | |
| 2008 | 1638 ( 16.9) | |
| 2009 | 1668 ( 17.2) | |
| 2010 | 1739 ( 18.0) | |
| 2011 | 1765 ( 18.2) |
Question 1: Who has long discharge delays?
| variable_name | level | [ 0, 4) | [ 4, 8) | [ 8, 24) | [ 24,130] | p | test |
|---|---|---|---|---|---|---|---|
| n | 1308 | 4348 | 3006 | 1011 | |||
| micu (%) | 1 | 1308 (100.0) | 4348 (100.0) | 3006 (100.0) | 1011 (100.0) | NA | |
| age (mean (sd)) | 63.11 (17.86) | 63.51 (17.92) | 64.04 (18.02) | 64.12 (17.91) | 0.309 | ||
| callout_month (%) | 1 | 113 ( 8.6) | 334 ( 7.7) | 238 ( 7.9) | 89 ( 8.8) | <0.001 | |
| 2 | 95 ( 7.3) | 296 ( 6.8) | 257 ( 8.5) | 90 ( 8.9) | |||
| 3 | 105 ( 8.0) | 365 ( 8.4) | 212 ( 7.1) | 51 ( 5.0) | |||
| 4 | 124 ( 9.5) | 354 ( 8.1) | 193 ( 6.4) | 55 ( 5.4) | |||
| 5 | 103 ( 7.9) | 374 ( 8.6) | 251 ( 8.3) | 66 ( 6.5) | |||
| 6 | 110 ( 8.4) | 347 ( 8.0) | 214 ( 7.1) | 74 ( 7.3) | |||
| 7 | 104 ( 8.0) | 371 ( 8.5) | 233 ( 7.8) | 71 ( 7.0) | |||
| 8 | 99 ( 7.6) | 379 ( 8.7) | 290 ( 9.6) | 109 ( 10.8) | |||
| 9 | 105 ( 8.0) | 328 ( 7.5) | 292 ( 9.7) | 126 ( 12.5) | |||
| 10 | 112 ( 8.6) | 379 ( 8.7) | 309 ( 10.3) | 114 ( 11.3) | |||
| 11 | 100 ( 7.6) | 359 ( 8.3) | 257 ( 8.5) | 107 ( 10.6) | |||
| 12 | 138 ( 10.6) | 462 ( 10.6) | 260 ( 8.6) | 59 ( 5.8) | |||
| female (%) | 0 | 682 ( 52.1) | 2205 ( 50.7) | 1523 ( 50.7) | 546 ( 54.0) | 0.221 | |
| 1 | 626 ( 47.9) | 2143 ( 49.3) | 1483 ( 49.3) | 465 ( 46.0) | |||
| request_tele (%) | 0 | 808 ( 61.8) | 2836 ( 65.2) | 2004 ( 66.7) | 670 ( 66.3) | 0.018 | |
| 1 | 500 ( 38.2) | 1512 ( 34.8) | 1002 ( 33.3) | 341 ( 33.7) | |||
| request_resp (%) | 0 | 1292 ( 98.8) | 4274 ( 98.3) | 2955 ( 98.3) | 993 ( 98.2) | 0.639 | |
| 1 | 16 ( 1.2) | 74 ( 1.7) | 51 ( 1.7) | 18 ( 1.8) | |||
| request_cdiff (%) | 0 | 1257 ( 96.1) | 4139 ( 95.2) | 2854 ( 94.9) | 940 ( 93.0) | 0.006 | |
| 1 | 51 ( 3.9) | 209 ( 4.8) | 152 ( 5.1) | 71 ( 7.0) | |||
| request_mrsa (%) | 0 | 1184 ( 90.5) | 3804 ( 87.5) | 2588 ( 86.1) | 829 ( 82.0) | <0.001 | |
| 1 | 124 ( 9.5) | 544 ( 12.5) | 418 ( 13.9) | 182 ( 18.0) | |||
| request_vre (%) | 0 | 1235 ( 94.4) | 4144 ( 95.3) | 2860 ( 95.1) | 943 ( 93.3) | 0.045 | |
| 1 | 73 ( 5.6) | 204 ( 4.7) | 146 ( 4.9) | 68 ( 6.7) | |||
| oasis (mean (sd)) | 29.38 (7.22) | 29.78 (7.37) | 30.01 (7.15) | 30.21 (7.49) | 0.021 | ||
| elixhauser_hospital (mean (sd)) | 3.30 (7.12) | 3.49 (7.23) | 3.52 (7.35) | 4.15 (7.45) | 0.031 | ||
| ethnicity (%) | White | 962 ( 73.5) | 3107 ( 71.5) | 2114 ( 70.3) | 727 ( 71.9) | 0.534 | |
| African American/Black | 190 ( 14.5) | 673 ( 15.5) | 481 ( 16.0) | 150 ( 14.8) | |||
| Other | 156 ( 11.9) | 568 ( 13.1) | 411 ( 13.7) | 134 ( 13.3) | |||
| MED_SERVICE (%) | FALSE | 155 ( 11.9) | 327 ( 7.5) | 156 ( 5.2) | 54 ( 5.3) | <0.001 | |
| TRUE | 1153 ( 88.1) | 4021 ( 92.5) | 2850 ( 94.8) | 957 ( 94.7) | |||
| HOSP_FREE_DAYS (median [IQR]) | 24.05 [20.72, 25.98] | 24.05 [20.18, 25.97] | 24.25 [21.03, 26.09] | 24.16 [19.63, 26.23] | <0.001 | nonnorm | |
| callout_dayofweek (%) | friday | 165 ( 12.6) | 641 ( 14.7) | 480 ( 16.0) | 156 ( 15.4) | <0.001 | |
| monday | 208 ( 15.9) | 612 ( 14.1) | 370 ( 12.3) | 110 ( 10.9) | |||
| saturday | 146 ( 11.2) | 594 ( 13.7) | 442 ( 14.7) | 110 ( 10.9) | |||
| sunday | 234 ( 17.9) | 611 ( 14.1) | 326 ( 10.8) | 96 ( 9.5) | |||
| thursday | 172 ( 13.1) | 580 ( 13.3) | 467 ( 15.5) | 193 ( 19.1) | |||
| tuesday | 185 ( 14.1) | 618 ( 14.2) | 450 ( 15.0) | 169 ( 16.7) | |||
| wednesday | 198 ( 15.1) | 692 ( 15.9) | 471 ( 15.7) | 177 ( 17.5) | |||
| CALLOUT_DURING_NIGHT (%) | FALSE | 1293 ( 98.9) | 4331 ( 99.6) | 2976 ( 99.0) | 1007 ( 99.6) | 0.002 | |
| TRUE | 15 ( 1.1) | 17 ( 0.4) | 30 ( 1.0) | 4 ( 0.4) | |||
| CALLOUT_DURING_ROUNDS (%) | FALSE | 765 ( 58.5) | 1744 ( 40.1) | 862 ( 28.7) | 445 ( 44.0) | <0.001 | |
| TRUE | 543 ( 41.5) | 2604 ( 59.9) | 2144 ( 71.3) | 566 ( 56.0) | |||
| DISCHARGEDELAY_HOURS (mean (sd)) | 3.07 (0.70) | 5.95 (1.11) | 11.40 (3.49) | 35.80 (12.87) | <0.001 | ||
| hourofcallout2 (median [IQR]) | 12.60 [10.71, 15.25] | 11.52 [10.35, 13.07] | 10.72 [9.47, 12.17] | 11.60 [10.39, 13.88] | <0.001 | nonnorm | |
| PROPFULL_BEDS (mean (sd)) | 0.89 (0.09) | 0.90 (0.09) | 0.92 (0.08) | 0.95 (0.07) | <0.001 | ||
| postcalldaycat2 (%) | 0 | 1284 ( 98.2) | 4211 ( 96.8) | 2305 ( 76.7) | 0 ( 0.0) | <0.001 | |
| [1,5] | 24 ( 1.8) | 137 ( 3.2) | 701 ( 23.3) | 1011 (100.0) | |||
| hospitaldeath (%) | 0 | 1233 ( 94.3) | 4117 ( 94.7) | 2848 ( 94.7) | 942 ( 93.2) | 0.243 | |
| 1 | 75 ( 5.7) | 231 ( 5.3) | 158 ( 5.3) | 69 ( 6.8) | |||
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.38] | 0.00 [0.00, 0.15] | 0.00 [0.00, 0.10] | 0.00 [0.00, 0.07] | 0.012 | nonnorm | |
| los_post_callout_days (median [IQR]) | 3.97 [2.11, 6.99] | 4.14 [2.26, 7.18] | 4.14 [2.31, 7.17] | 5.13 [3.11, 9.06] | <0.001 | nonnorm | |
| los_post_icu_days (median [IQR]) | 3.83 [1.97, 6.85] | 3.89 [2.00, 6.94] | 3.67 [1.88, 6.71] | 3.71 [1.68, 7.33] | <0.001 | nonnorm | |
| los_pre_callout_days (median [IQR]) | 1.80 [0.98, 3.57] | 1.78 [0.95, 3.72] | 1.76 [0.89, 3.62] | 1.73 [0.88, 3.76] | 0.391 | nonnorm | |
| callout_year (%) | 2005 | 27 ( 2.1) | 164 ( 3.8) | 113 ( 3.8) | 72 ( 7.1) | <0.001 | |
| 2006 | 102 ( 7.8) | 404 ( 9.3) | 297 ( 9.9) | 267 ( 26.4) | |||
| 2007 | 140 ( 10.7) | 521 ( 12.0) | 474 ( 15.8) | 282 ( 27.9) | |||
| 2008 | 229 ( 17.5) | 786 ( 18.1) | 516 ( 17.2) | 107 ( 10.6) | |||
| 2009 | 308 ( 23.5) | 811 ( 18.7) | 475 ( 15.8) | 74 ( 7.3) | |||
| 2010 | 222 ( 17.0) | 779 ( 17.9) | 608 ( 20.2) | 130 ( 12.9) | |||
| 2011 | 280 ( 21.4) | 883 ( 20.3) | 523 ( 17.4) | 79 ( 7.8) |
Determinants of the DD are quite complex, depending on many factors. We instead focus on breaking down DD to >=24 vs <24h
| variable_name | level | [ 0.238, 24.000) | [ 24.000,129.566] | p | test |
|---|---|---|---|---|---|
| n | 8662 | 1011 | |||
| micu (%) | 1 | 8662 (100.0) | 1011 (100.0) | NA | |
| age (mean (sd)) | 63.63 (17.95) | 64.12 (17.91) | 0.414 | ||
| callout_month (%) | 1 | 685 ( 7.9) | 89 ( 8.8) | <0.001 | |
| 2 | 648 ( 7.5) | 90 ( 8.9) | |||
| 3 | 682 ( 7.9) | 51 ( 5.0) | |||
| 4 | 671 ( 7.7) | 55 ( 5.4) | |||
| 5 | 728 ( 8.4) | 66 ( 6.5) | |||
| 6 | 671 ( 7.7) | 74 ( 7.3) | |||
| 7 | 708 ( 8.2) | 71 ( 7.0) | |||
| 8 | 768 ( 8.9) | 109 ( 10.8) | |||
| 9 | 725 ( 8.4) | 126 ( 12.5) | |||
| 10 | 800 ( 9.2) | 114 ( 11.3) | |||
| 11 | 716 ( 8.3) | 107 ( 10.6) | |||
| 12 | 860 ( 9.9) | 59 ( 5.8) | |||
| female (%) | 0 | 4410 ( 50.9) | 546 ( 54.0) | 0.067 | |
| 1 | 4252 ( 49.1) | 465 ( 46.0) | |||
| request_tele (%) | 0 | 5648 ( 65.2) | 670 ( 66.3) | 0.523 | |
| 1 | 3014 ( 34.8) | 341 ( 33.7) | |||
| request_resp (%) | 0 | 8521 ( 98.4) | 993 ( 98.2) | 0.818 | |
| 1 | 141 ( 1.6) | 18 ( 1.8) | |||
| request_cdiff (%) | 0 | 8250 ( 95.2) | 940 ( 93.0) | 0.002 | |
| 1 | 412 ( 4.8) | 71 ( 7.0) | |||
| request_mrsa (%) | 0 | 7576 ( 87.5) | 829 ( 82.0) | <0.001 | |
| 1 | 1086 ( 12.5) | 182 ( 18.0) | |||
| request_vre (%) | 0 | 8239 ( 95.1) | 943 ( 93.3) | 0.014 | |
| 1 | 423 ( 4.9) | 68 ( 6.7) | |||
| oasis (mean (sd)) | 29.80 (7.27) | 30.21 (7.49) | 0.085 | ||
| elixhauser_hospital (mean (sd)) | 3.47 (7.25) | 4.15 (7.45) | 0.005 | ||
| ethnicity (%) | White | 6183 ( 71.4) | 727 ( 71.9) | 0.851 | |
| African American/Black | 1344 ( 15.5) | 150 ( 14.8) | |||
| Other | 1135 ( 13.1) | 134 ( 13.3) | |||
| MED_SERVICE (%) | FALSE | 638 ( 7.4) | 54 ( 5.3) | 0.022 | |
| TRUE | 8024 ( 92.6) | 957 ( 94.7) | |||
| HOSP_FREE_DAYS (median [IQR]) | 24.11 [20.35, 26.01] | 24.16 [19.63, 26.23] | 0.035 | nonnorm | |
| callout_dayofweek (%) | friday | 1286 ( 14.8) | 156 ( 15.4) | <0.001 | |
| monday | 1190 ( 13.7) | 110 ( 10.9) | |||
| saturday | 1182 ( 13.6) | 110 ( 10.9) | |||
| sunday | 1171 ( 13.5) | 96 ( 9.5) | |||
| thursday | 1219 ( 14.1) | 193 ( 19.1) | |||
| tuesday | 1253 ( 14.5) | 169 ( 16.7) | |||
| wednesday | 1361 ( 15.7) | 177 ( 17.5) | |||
| CALLOUT_DURING_NIGHT (%) | FALSE | 8600 ( 99.3) | 1007 ( 99.6) | 0.333 | |
| TRUE | 62 ( 0.7) | 4 ( 0.4) | |||
| CALLOUT_DURING_ROUNDS (%) | FALSE | 3371 ( 38.9) | 445 ( 44.0) | 0.002 | |
| TRUE | 5291 ( 61.1) | 566 ( 56.0) | |||
| DISCHARGEDELAY_HOURS (mean (sd)) | 7.40 (3.79) | 35.80 (12.87) | <0.001 | ||
| hourofcallout2 (median [IQR]) | 11.37 [10.08, 13.08] | 11.60 [10.39, 13.88] | <0.001 | nonnorm | |
| PROPFULL_BEDS (mean (sd)) | 0.91 (0.09) | 0.95 (0.07) | <0.001 | ||
| postcalldaycat2 (%) | 0 | 7800 ( 90.0) | 0 ( 0.0) | <0.001 | |
| [1,5] | 862 ( 10.0) | 1011 (100.0) | |||
| hospitaldeath (%) | 0 | 8198 ( 94.6) | 942 ( 93.2) | 0.062 | |
| 1 | 464 ( 5.4) | 69 ( 6.8) | |||
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.16] | 0.00 [0.00, 0.07] | 0.459 | nonnorm | |
| los_post_callout_days (median [IQR]) | 4.12 [2.26, 7.15] | 5.13 [3.11, 9.06] | <0.001 | nonnorm | |
| los_post_icu_days (median [IQR]) | 3.81 [1.97, 6.86] | 3.71 [1.68, 7.33] | 0.012 | nonnorm | |
| los_pre_callout_days (median [IQR]) | 1.77 [0.94, 3.68] | 1.73 [0.88, 3.76] | 0.541 | nonnorm | |
| callout_year (%) | 2005 | 304 ( 3.5) | 72 ( 7.1) | <0.001 | |
| 2006 | 803 ( 9.3) | 267 ( 26.4) | |||
| 2007 | 1135 ( 13.1) | 282 ( 27.9) | |||
| 2008 | 1531 ( 17.7) | 107 ( 10.6) | |||
| 2009 | 1594 ( 18.4) | 74 ( 7.3) | |||
| 2010 | 1609 ( 18.6) | 130 ( 12.9) | |||
| 2011 | 1686 ( 19.5) | 79 ( 7.8) |
We fit a logistic regression model for DD>24 as the “outcome” with:
- Demographics: age, sex,
- Requests: tele, resp, mrsa, cdiff, vre
- Adjustment for severity/comoribidity/icu los: oasis, exlixhauser, los_pre_callout_days
- Possible structural/admin variables: DOW, month, year.
- Census variables: type of bed requested, proportion of hospital beds is use.
as covariates.
Model selection: fit full model, reduce backwards stepwise, until all variables are significant by LRT.
AIC/BIC model selection was done first, but used only as a comparison to the manually worked selection (jdr.ddelay.glm).
## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + cut2(age, g = 3) + female + request_tele + request_resp +
## request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + cut2(hourofcallout2, c(7, 12, 19))
## Df Deviance
## <none> 5521.7
## cut2(oasis, g = 3) 2 5525.3
## cut2(age, g = 3) 2 5522.4
## female 1 5527.5
## request_tele 1 5523.3
## request_resp 1 5522.7
## request_mrsa 1 5544.2
## request_vre 1 5526.1
## request_cdiff 1 5532.7
## cut2(elixhauser_hospital, g = 3) 2 5522.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5530.0
## as.factor(callout_month) 11 5586.8
## as.factor(callout_year) 6 6077.5
## as.factor(callout_dayofweek) 6 5580.4
## MED_SERVICE 1 5522.4
## cut2(hourofcallout2, c(7, 12, 19)) 3 5539.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5534.5
## AIC
## <none> 5619.7
## cut2(oasis, g = 3) 5619.3
## cut2(age, g = 3) 5616.4
## female 5623.5
## request_tele 5619.3
## request_resp 5618.7
## request_mrsa 5640.2
## request_vre 5622.1
## request_cdiff 5628.7
## cut2(elixhauser_hospital, g = 3) 5616.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5620.0
## as.factor(callout_month) 5662.8
## as.factor(callout_year) 6163.5
## as.factor(callout_dayofweek) 5666.4
## MED_SERVICE 5618.4
## cut2(hourofcallout2, c(7, 12, 19)) 5631.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5628.5
## LRT
## <none>
## cut2(oasis, g = 3) 3.61
## cut2(age, g = 3) 0.68
## female 5.80
## request_tele 1.53
## request_resp 0.98
## request_mrsa 22.48
## request_vre 4.35
## request_cdiff 10.98
## cut2(elixhauser_hospital, g = 3) 0.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.24
## as.factor(callout_month) 65.11
## as.factor(callout_year) 555.79
## as.factor(callout_dayofweek) 58.67
## MED_SERVICE 0.68
## cut2(hourofcallout2, c(7, 12, 19)) 17.93
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.77
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1643833
## cut2(age, g = 3) 0.7131505
## female 0.0160151
## request_tele 0.2162413
## request_resp 0.3232036
## request_mrsa 2.127e-06
## request_vre 0.0370250
## request_cdiff 0.0009210
## cut2(elixhauser_hospital, g = 3) 0.6506463
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0833378
## as.factor(callout_month) 1.029e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.366e-11
## MED_SERVICE 0.4094765
## cut2(hourofcallout2, c(7, 12, 19)) 0.0004549
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0016855
##
## <none>
## cut2(oasis, g = 3)
## cut2(age, g = 3)
## female *
## request_tele
## request_resp
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3)
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## MED_SERVICE
## cut2(hourofcallout2, c(7, 12, 19)) ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ request_mrsa +
## request_cdiff + as.factor(callout_year) + cut2(PROPFULL_BEDS,
## c(0.9, 1))
## Df Deviance AIC LRT Pr(>Chi)
## <none> 5701.7 5723.7
## request_mrsa 1 5730.9 5750.9 29.17 6.616e-08 ***
## request_cdiff 1 5714.5 5734.5 12.81 0.0003452 ***
## as.factor(callout_year) 6 6241.0 6251.0 539.24 < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5909.1 5927.1 207.43 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_mrsa + request_vre + request_cdiff +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + cut2(hourofcallout2, c(7, 12, 19)) + as.factor(callout_wardid ==
## 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## Df Deviance
## <none> 5526.4
## cut2(oasis, g = 3) 2 5530.8
## female 1 5532.9
## request_mrsa 1 5549.5
## request_vre 1 5531.1
## request_cdiff 1 5537.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5534.6
## as.factor(callout_month) 11 5591.2
## as.factor(callout_year) 6 6085.3
## as.factor(callout_dayofweek) 6 5585.0
## cut2(hourofcallout2, c(7, 12, 19)) 3 5544.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5539.4
## AIC
## <none> 5610.4
## cut2(oasis, g = 3) 5610.8
## female 5614.9
## request_mrsa 5631.5
## request_vre 5613.1
## request_cdiff 5619.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5610.6
## as.factor(callout_month) 5653.2
## as.factor(callout_year) 6157.3
## as.factor(callout_dayofweek) 5657.0
## cut2(hourofcallout2, c(7, 12, 19)) 5622.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5619.4
## LRT
## <none>
## cut2(oasis, g = 3) 4.38
## female 6.51
## request_mrsa 23.08
## request_vre 4.65
## request_cdiff 11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.17
## as.factor(callout_month) 64.80
## as.factor(callout_year) 558.90
## as.factor(callout_dayofweek) 58.61
## cut2(hourofcallout2, c(7, 12, 19)) 17.88
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.95
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1121047
## female 0.0107553
## request_mrsa 1.557e-06
## request_vre 0.0310622
## request_cdiff 0.0007885
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0855801
## as.factor(callout_month) 1.173e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.628e-11
## cut2(hourofcallout2, c(7, 12, 19)) 0.0004660
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015379
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## cut2(hourofcallout2, c(7, 12, 19)) ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + cut2(age, g = 3) + female + request_tele + request_resp +
## request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7,
## 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5521.7
## cut2(oasis, g = 3) 2 5525.3
## cut2(age, g = 3) 2 5522.4
## female 1 5527.5
## request_tele 1 5523.3
## request_resp 1 5522.7
## request_mrsa 1 5544.2
## request_vre 1 5526.1
## request_cdiff 1 5532.7
## cut2(elixhauser_hospital, g = 3) 2 5522.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5530.0
## as.factor(callout_month) 11 5586.8
## as.factor(callout_year) 6 6077.5
## as.factor(callout_dayofweek) 6 5580.4
## MED_SERVICE 1 5522.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5539.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5534.5
## AIC
## <none> 5619.7
## cut2(oasis, g = 3) 5619.3
## cut2(age, g = 3) 5616.4
## female 5623.5
## request_tele 5619.3
## request_resp 5618.7
## request_mrsa 5640.2
## request_vre 5622.1
## request_cdiff 5628.7
## cut2(elixhauser_hospital, g = 3) 5616.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5620.0
## as.factor(callout_month) 5662.8
## as.factor(callout_year) 6163.5
## as.factor(callout_dayofweek) 5666.4
## MED_SERVICE 5618.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5631.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5628.5
## LRT
## <none>
## cut2(oasis, g = 3) 3.61
## cut2(age, g = 3) 0.68
## female 5.80
## request_tele 1.53
## request_resp 0.98
## request_mrsa 22.48
## request_vre 4.35
## request_cdiff 10.98
## cut2(elixhauser_hospital, g = 3) 0.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.24
## as.factor(callout_month) 65.11
## as.factor(callout_year) 555.79
## as.factor(callout_dayofweek) 58.67
## MED_SERVICE 0.68
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 17.93
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.77
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1643833
## cut2(age, g = 3) 0.7131505
## female 0.0160151
## request_tele 0.2162413
## request_resp 0.3232036
## request_mrsa 2.127e-06
## request_vre 0.0370250
## request_cdiff 0.0009210
## cut2(elixhauser_hospital, g = 3) 0.6506463
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0833378
## as.factor(callout_month) 1.029e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.366e-11
## MED_SERVICE 0.4094765
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004549
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0016855
##
## <none>
## cut2(oasis, g = 3)
## cut2(age, g = 3)
## female *
## request_tele
## request_resp
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3)
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(age, g = 3)"## [1] 0.7131505## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS,
## c(0.9, 1))
## Df Deviance
## <none> 5522.4
## cut2(oasis, g = 3) 2 5525.8
## female 1 5528.4
## request_tele 1 5523.9
## request_resp 1 5523.4
## request_mrsa 1 5545.0
## request_vre 1 5527.0
## request_cdiff 1 5533.3
## cut2(elixhauser_hospital, g = 3) 2 5523.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5530.7
## as.factor(callout_month) 11 5587.9
## as.factor(callout_year) 6 6077.9
## as.factor(callout_dayofweek) 6 5581.1
## MED_SERVICE 1 5523.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5540.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5535.3
## AIC
## <none> 5616.4
## cut2(oasis, g = 3) 5615.8
## female 5620.4
## request_tele 5615.9
## request_resp 5615.4
## request_mrsa 5637.0
## request_vre 5619.0
## request_cdiff 5625.3
## cut2(elixhauser_hospital, g = 3) 5613.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5616.7
## as.factor(callout_month) 5659.9
## as.factor(callout_year) 6159.9
## as.factor(callout_dayofweek) 5663.1
## MED_SERVICE 5615.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5628.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5625.3
## LRT
## <none>
## cut2(oasis, g = 3) 3.40
## female 5.96
## request_tele 1.48
## request_resp 1.03
## request_mrsa 22.61
## request_vre 4.58
## request_cdiff 10.92
## cut2(elixhauser_hospital, g = 3) 0.73
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.28
## as.factor(callout_month) 65.45
## as.factor(callout_year) 555.54
## as.factor(callout_dayofweek) 58.67
## MED_SERVICE 0.71
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 17.89
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.88
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1828121
## female 0.0146237
## request_tele 0.2232544
## request_resp 0.3109412
## request_mrsa 1.988e-06
## request_vre 0.0323407
## request_cdiff 0.0009537
## cut2(elixhauser_hospital, g = 3) 0.6940781
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0819099
## as.factor(callout_month) 8.878e-10
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.397e-11
## MED_SERVICE 0.3985511
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004629
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015927
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_tele
## request_resp
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3)
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(elixhauser_hospital, g = 3)"## [1] 0.6940781## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid ==
## 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## Df Deviance
## <none> 5523.1
## cut2(oasis, g = 3) 2 5527.1
## female 1 5529.5
## request_tele 1 5524.8
## request_resp 1 5524.2
## request_mrsa 1 5545.8
## request_vre 1 5527.9
## request_cdiff 1 5534.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5531.2
## as.factor(callout_month) 11 5588.7
## as.factor(callout_year) 6 6080.9
## as.factor(callout_dayofweek) 6 5581.8
## MED_SERVICE 1 5523.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5541.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5535.9
## AIC
## <none> 5613.1
## cut2(oasis, g = 3) 5613.1
## female 5617.5
## request_tele 5612.8
## request_resp 5612.2
## request_mrsa 5633.8
## request_vre 5615.9
## request_cdiff 5622.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5613.2
## as.factor(callout_month) 5656.7
## as.factor(callout_year) 6158.9
## as.factor(callout_dayofweek) 5659.8
## MED_SERVICE 5611.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5625.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5621.9
## LRT
## <none>
## cut2(oasis, g = 3) 3.97
## female 6.34
## request_tele 1.62
## request_resp 1.02
## request_mrsa 22.63
## request_vre 4.74
## request_cdiff 11.17
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.07
## as.factor(callout_month) 65.53
## as.factor(callout_year) 557.72
## as.factor(callout_dayofweek) 58.71
## MED_SERVICE 0.79
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 18.12
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.76
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1370424
## female 0.0117737
## request_tele 0.2032677
## request_resp 0.3130016
## request_mrsa 1.966e-06
## request_vre 0.0295421
## request_cdiff 0.0008301
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0889496
## as.factor(callout_month) 8.567e-10
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.222e-11
## MED_SERVICE 0.3744853
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004164
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0016993
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_tele
## request_resp
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "MED_SERVICE"## [1] 0.3744853## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid ==
## 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## Df Deviance
## <none> 5523.9
## cut2(oasis, g = 3) 2 5528.1
## female 1 5530.3
## request_tele 1 5525.5
## request_resp 1 5525.0
## request_mrsa 1 5546.9
## request_vre 1 5528.7
## request_cdiff 1 5535.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5532.0
## as.factor(callout_month) 11 5589.5
## as.factor(callout_year) 6 6084.6
## as.factor(callout_dayofweek) 6 5582.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5541.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5536.8
## AIC
## <none> 5611.9
## cut2(oasis, g = 3) 5612.1
## female 5616.3
## request_tele 5611.5
## request_resp 5611.0
## request_mrsa 5632.9
## request_vre 5614.7
## request_cdiff 5621.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5612.0
## as.factor(callout_month) 5655.5
## as.factor(callout_year) 6160.6
## as.factor(callout_dayofweek) 5658.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5623.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5620.8
## LRT
## <none>
## cut2(oasis, g = 3) 4.22
## female 6.33
## request_tele 1.53
## request_resp 1.05
## request_mrsa 23.00
## request_vre 4.80
## request_cdiff 11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.12
## as.factor(callout_month) 65.57
## as.factor(callout_year) 560.70
## as.factor(callout_dayofweek) 58.67
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 18.01
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.92
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1211694
## female 0.0118413
## request_tele 0.2159673
## request_resp 0.3050004
## request_mrsa 1.619e-06
## request_vre 0.0284797
## request_cdiff 0.0007868
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0871798
## as.factor(callout_month) 8.432e-10
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.371e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004371
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015647
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_tele
## request_resp
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_resp"## [1] 0.3050004## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_tele + request_mrsa + request_vre +
## request_cdiff + cut2(los_pre_callout_days, c(1, 3, 7, 28)) +
## as.factor(callout_month) + as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") +
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9,
## 1))
## Df Deviance
## <none> 5525.0
## cut2(oasis, g = 3) 2 5529.2
## female 1 5531.4
## request_tele 1 5526.4
## request_mrsa 1 5548.1
## request_vre 1 5529.7
## request_cdiff 1 5536.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5533.2
## as.factor(callout_month) 11 5590.0
## as.factor(callout_year) 6 6084.9
## as.factor(callout_dayofweek) 6 5583.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5542.8
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5537.9
## AIC
## <none> 5611.0
## cut2(oasis, g = 3) 5611.2
## female 5615.4
## request_tele 5610.4
## request_mrsa 5632.1
## request_vre 5613.7
## request_cdiff 5620.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5611.2
## as.factor(callout_month) 5654.0
## as.factor(callout_year) 6158.9
## as.factor(callout_dayofweek) 5657.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.8
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5619.9
## LRT
## <none>
## cut2(oasis, g = 3) 4.21
## female 6.39
## request_tele 1.46
## request_mrsa 23.10
## request_vre 4.74
## request_cdiff 11.29
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.23
## as.factor(callout_month) 65.01
## as.factor(callout_year) 559.90
## as.factor(callout_dayofweek) 58.60
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 17.85
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.94
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1220234
## female 0.0114725
## request_tele 0.2264985
## request_mrsa 1.537e-06
## request_vre 0.0294004
## request_cdiff 0.0007793
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0835985
## as.factor(callout_month) 1.074e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.658e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004722
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015491
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_tele
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_tele"## [1] 0.2264985## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ cut2(oasis,
## g = 3) + female + request_mrsa + request_vre + request_cdiff +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") +
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9,
## 1))
## Df Deviance
## <none> 5526.4
## cut2(oasis, g = 3) 2 5530.8
## female 1 5532.9
## request_mrsa 1 5549.5
## request_vre 1 5531.1
## request_cdiff 1 5537.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5534.6
## as.factor(callout_month) 11 5591.2
## as.factor(callout_year) 6 6085.3
## as.factor(callout_dayofweek) 6 5585.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5544.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5539.4
## AIC
## <none> 5610.4
## cut2(oasis, g = 3) 5610.8
## female 5614.9
## request_mrsa 5631.5
## request_vre 5613.1
## request_cdiff 5619.7
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5610.6
## as.factor(callout_month) 5653.2
## as.factor(callout_year) 6157.3
## as.factor(callout_dayofweek) 5657.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5619.4
## LRT
## <none>
## cut2(oasis, g = 3) 4.38
## female 6.51
## request_mrsa 23.08
## request_vre 4.65
## request_cdiff 11.27
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.17
## as.factor(callout_month) 64.80
## as.factor(callout_year) 558.90
## as.factor(callout_dayofweek) 58.61
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 17.88
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 12.95
## Pr(>Chi)
## <none>
## cut2(oasis, g = 3) 0.1121047
## female 0.0107553
## request_mrsa 1.557e-06
## request_vre 0.0310622
## request_cdiff 0.0007885
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0855801
## as.factor(callout_month) 1.173e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 8.628e-11
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004660
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0015379
##
## <none>
## cut2(oasis, g = 3)
## female *
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(oasis, g = 3)"## [1] 0.1121047## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ female +
## request_mrsa + request_vre + request_cdiff + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid ==
## 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## Df Deviance
## <none> 5530.8
## female 1 5537.0
## request_mrsa 1 5554.3
## request_vre 1 5535.5
## request_cdiff 1 5543.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5539.3
## as.factor(callout_month) 11 5595.4
## as.factor(callout_year) 6 6089.3
## as.factor(callout_dayofweek) 6 5588.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5549.0
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5544.1
## AIC
## <none> 5610.8
## female 5615.0
## request_mrsa 5632.3
## request_vre 5613.5
## request_cdiff 5621.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5611.3
## as.factor(callout_month) 5653.4
## as.factor(callout_year) 6157.3
## as.factor(callout_dayofweek) 5656.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5623.0
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5620.1
## LRT
## <none>
## female 6.23
## request_mrsa 23.47
## request_vre 4.69
## request_cdiff 12.48
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 8.45
## as.factor(callout_month) 64.60
## as.factor(callout_year) 558.50
## as.factor(callout_dayofweek) 57.84
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 18.13
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 13.30
## Pr(>Chi)
## <none>
## female 0.0125658
## request_mrsa 1.271e-06
## request_vre 0.0304175
## request_cdiff 0.0004111
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 0.0765210
## as.factor(callout_month) 1.281e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 1.234e-10
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0004127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0012971
##
## <none>
## female *
## request_mrsa ***
## request_vre *
## request_cdiff ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) .
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(los_pre_callout_days, c(1, 3, 7, 28))"## [1] 0.076521## Single term deletions
##
## Model:
## cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~ female +
## request_mrsa + request_vre + request_cdiff + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") +
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9,
## 1))
## Df Deviance
## <none> 5539.3
## female 1 5545.3
## request_mrsa 1 5562.3
## request_vre 1 5544.1
## request_cdiff 1 5551.4
## as.factor(callout_month) 11 5602.8
## as.factor(callout_year) 6 6095.4
## as.factor(callout_dayofweek) 6 5597.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5556.4
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5553.0
## AIC
## <none> 5611.3
## female 5615.3
## request_mrsa 5632.3
## request_vre 5614.1
## request_cdiff 5621.4
## as.factor(callout_month) 5652.8
## as.factor(callout_year) 6155.4
## as.factor(callout_dayofweek) 5657.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5622.4
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5621.0
## LRT
## <none>
## female 6.06
## request_mrsa 23.04
## request_vre 4.88
## request_cdiff 12.14
## as.factor(callout_month) 63.52
## as.factor(callout_year) 556.12
## as.factor(callout_dayofweek) 58.16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 17.15
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 13.70
## Pr(>Chi)
## <none>
## female 0.0138446
## request_mrsa 1.588e-06
## request_vre 0.0271121
## request_cdiff 0.0004928
## as.factor(callout_month) 2.047e-09
## as.factor(callout_year) < 2.2e-16
## as.factor(callout_dayofweek) 1.066e-10
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0006594
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0010603
##
## <none>
## female *
## request_mrsa ***
## request_vre *
## request_cdiff ***
## as.factor(callout_month) ***
## as.factor(callout_year) ***
## as.factor(callout_dayofweek) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") ***
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_vre"## [1] 0.0271121##
## Call:
## glm(formula = cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]" ~
## female + request_mrsa + request_vre + request_cdiff + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS,
## c(0.9, 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)),
## "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS,
## c(0.9, 1)), family = "binomial", data = d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4841 -0.4791 -0.3238 -0.2197 3.0144
##
## Coefficients:
## Estimate
## (Intercept) -1.73947
## female -0.17488
## request_mrsa 0.47921
## request_vre 0.34237
## request_cdiff 0.52787
## as.factor(callout_month)2 -0.01079
## as.factor(callout_month)3 -0.69402
## as.factor(callout_month)4 -0.39710
## as.factor(callout_month)5 -0.35628
## as.factor(callout_month)6 -0.47344
## as.factor(callout_month)7 -0.12734
## as.factor(callout_month)8 0.12819
## as.factor(callout_month)9 0.24047
## as.factor(callout_month)10 0.08107
## as.factor(callout_month)11 0.07312
## as.factor(callout_month)12 -0.54476
## as.factor(callout_year)2006 0.50025
## as.factor(callout_year)2007 -0.09478
## as.factor(callout_year)2008 -1.54972
## as.factor(callout_year)2009 -1.38900
## as.factor(callout_year)2010 -1.38651
## as.factor(callout_year)2011 -1.66868
## as.factor(callout_dayofweek)monday -0.45487
## as.factor(callout_dayofweek)saturday 0.59972
## as.factor(callout_dayofweek)sunday 0.44095
## as.factor(callout_dayofweek)thursday -0.10873
## as.factor(callout_dayofweek)tuesday -0.34804
## as.factor(callout_dayofweek)wednesday -0.48457
## as.factor(callout_wardid == 1)TRUE -0.66724
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.84106
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.20867
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.19134
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.30378
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.09684
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.41306
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.04129
## Std. Error
## (Intercept) 0.28215
## female 0.07116
## request_mrsa 0.09707
## request_vre 0.15099
## request_cdiff 0.14539
## as.factor(callout_month)2 0.16842
## as.factor(callout_month)3 0.19323
## as.factor(callout_month)4 0.18936
## as.factor(callout_month)5 0.18038
## as.factor(callout_month)6 0.17818
## as.factor(callout_month)7 0.17706
## as.factor(callout_month)8 0.16360
## as.factor(callout_month)9 0.16081
## as.factor(callout_month)10 0.16297
## as.factor(callout_month)11 0.16509
## as.factor(callout_month)12 0.18787
## as.factor(callout_year)2006 0.15909
## as.factor(callout_year)2007 0.16074
## as.factor(callout_year)2008 0.17998
## as.factor(callout_year)2009 0.18773
## as.factor(callout_year)2010 0.17354
## as.factor(callout_year)2011 0.18513
## as.factor(callout_dayofweek)monday 0.14004
## as.factor(callout_dayofweek)saturday 0.15988
## as.factor(callout_dayofweek)sunday 0.16392
## as.factor(callout_dayofweek)thursday 0.12697
## as.factor(callout_dayofweek)tuesday 0.12982
## as.factor(callout_dayofweek)wednesday 0.13060
## as.factor(callout_wardid == 1)TRUE 0.18879
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.22962
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.28512
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.53598
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.07308
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.25931
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.23491
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.28515
## z value
## (Intercept) -6.165
## female -2.458
## request_mrsa 4.937
## request_vre 2.267
## request_cdiff 3.631
## as.factor(callout_month)2 -0.064
## as.factor(callout_month)3 -3.592
## as.factor(callout_month)4 -2.097
## as.factor(callout_month)5 -1.975
## as.factor(callout_month)6 -2.657
## as.factor(callout_month)7 -0.719
## as.factor(callout_month)8 0.784
## as.factor(callout_month)9 1.495
## as.factor(callout_month)10 0.497
## as.factor(callout_month)11 0.443
## as.factor(callout_month)12 -2.900
## as.factor(callout_year)2006 3.144
## as.factor(callout_year)2007 -0.590
## as.factor(callout_year)2008 -8.611
## as.factor(callout_year)2009 -7.399
## as.factor(callout_year)2010 -7.990
## as.factor(callout_year)2011 -9.013
## as.factor(callout_dayofweek)monday -3.248
## as.factor(callout_dayofweek)saturday 3.751
## as.factor(callout_dayofweek)sunday 2.690
## as.factor(callout_dayofweek)thursday -0.856
## as.factor(callout_dayofweek)tuesday -2.681
## as.factor(callout_dayofweek)wednesday -3.710
## as.factor(callout_wardid == 1)TRUE -3.534
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 3.663
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 4.239
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.357
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 4.157
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.373
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 1.758
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 3.652
## Pr(>|z|)
## (Intercept) 7.05e-10
## female 0.013986
## request_mrsa 7.94e-07
## request_vre 0.023360
## request_cdiff 0.000283
## as.factor(callout_month)2 0.948912
## as.factor(callout_month)3 0.000329
## as.factor(callout_month)4 0.035985
## as.factor(callout_month)5 0.048249
## as.factor(callout_month)6 0.007880
## as.factor(callout_month)7 0.472029
## as.factor(callout_month)8 0.433300
## as.factor(callout_month)9 0.134803
## as.factor(callout_month)10 0.618884
## as.factor(callout_month)11 0.657844
## as.factor(callout_month)12 0.003735
## as.factor(callout_year)2006 0.001664
## as.factor(callout_year)2007 0.555436
## as.factor(callout_year)2008 < 2e-16
## as.factor(callout_year)2009 1.37e-13
## as.factor(callout_year)2010 1.35e-15
## as.factor(callout_year)2011 < 2e-16
## as.factor(callout_dayofweek)monday 0.001162
## as.factor(callout_dayofweek)saturday 0.000176
## as.factor(callout_dayofweek)sunday 0.007146
## as.factor(callout_dayofweek)thursday 0.391775
## as.factor(callout_dayofweek)tuesday 0.007339
## as.factor(callout_dayofweek)wednesday 0.000207
## as.factor(callout_wardid == 1)TRUE 0.000409
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.000249
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 2.24e-05
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.721092
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 3.23e-05
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.708815
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.078688
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.000260
##
## (Intercept) ***
## female *
## request_mrsa ***
## request_vre *
## request_cdiff ***
## as.factor(callout_month)2
## as.factor(callout_month)3 ***
## as.factor(callout_month)4 *
## as.factor(callout_month)5 *
## as.factor(callout_month)6 **
## as.factor(callout_month)7
## as.factor(callout_month)8
## as.factor(callout_month)9
## as.factor(callout_month)10
## as.factor(callout_month)11
## as.factor(callout_month)12 **
## as.factor(callout_year)2006 **
## as.factor(callout_year)2007
## as.factor(callout_year)2008 ***
## as.factor(callout_year)2009 ***
## as.factor(callout_year)2010 ***
## as.factor(callout_year)2011 ***
## as.factor(callout_dayofweek)monday **
## as.factor(callout_dayofweek)saturday ***
## as.factor(callout_dayofweek)sunday **
## as.factor(callout_dayofweek)thursday
## as.factor(callout_dayofweek)tuesday **
## as.factor(callout_dayofweek)wednesday ***
## as.factor(callout_wardid == 1)TRUE ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000)
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) .
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6478.9 on 9672 degrees of freedom
## Residual deviance: 5539.3 on 9637 degrees of freedom
## AIC: 5611.3
##
## Number of Fisher Scoring iterations: 6| cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]” | ||||
| Odds Ratio | CI | p | ||
| (Intercept) | 0.18 | 0.10 – 0.30 | <.001 | |
| female | 0.84 | 0.73 – 0.97 | .014 | |
| request_mrsa | 1.61 | 1.33 – 1.95 | <.001 | |
| request_vre | 1.41 | 1.04 – 1.88 | .023 | |
| request_cdiff | 1.70 | 1.27 – 2.24 | <.001 | |
| as.factor(callout_month) | ||||
| as.factor(callout_month)2 | 0.99 | 0.71 – 1.38 | .949 | |
| as.factor(callout_month)3 | 0.50 | 0.34 – 0.73 | <.001 | |
| as.factor(callout_month)4 | 0.67 | 0.46 – 0.97 | .036 | |
| as.factor(callout_month)5 | 0.70 | 0.49 – 1.00 | .048 | |
| as.factor(callout_month)6 | 0.62 | 0.44 – 0.88 | .008 | |
| as.factor(callout_month)7 | 0.88 | 0.62 – 1.24 | .472 | |
| as.factor(callout_month)8 | 1.14 | 0.83 – 1.57 | .433 | |
| as.factor(callout_month)9 | 1.27 | 0.93 – 1.75 | .135 | |
| as.factor(callout_month)10 | 1.08 | 0.79 – 1.49 | .619 | |
| as.factor(callout_month)11 | 1.08 | 0.78 – 1.49 | .658 | |
| as.factor(callout_month)12 | 0.58 | 0.40 – 0.84 | .004 | |
| as.factor(callout_year) | ||||
| as.factor(callout_year)2006 | 1.65 | 1.21 – 2.26 | .002 | |
| as.factor(callout_year)2007 | 0.91 | 0.67 – 1.25 | .555 | |
| as.factor(callout_year)2008 | 0.21 | 0.15 – 0.30 | <.001 | |
| as.factor(callout_year)2009 | 0.25 | 0.17 – 0.36 | <.001 | |
| as.factor(callout_year)2010 | 0.25 | 0.18 – 0.35 | <.001 | |
| as.factor(callout_year)2011 | 0.19 | 0.13 – 0.27 | <.001 | |
| as.factor(callout_dayofweek) | ||||
| as.factor(callout_dayofweek)monday | 0.63 | 0.48 – 0.83 | .001 | |
| as.factor(callout_dayofweek)saturday | 1.82 | 1.33 – 2.49 | <.001 | |
| as.factor(callout_dayofweek)sunday | 1.55 | 1.13 – 2.14 | .007 | |
| as.factor(callout_dayofweek)thursday | 0.90 | 0.70 – 1.15 | .392 | |
| as.factor(callout_dayofweek)tuesday | 0.71 | 0.55 – 0.91 | .007 | |
| as.factor(callout_dayofweek)wednesday | 0.62 | 0.48 – 0.80 | <.001 | |
| as.factor(callout_wardid == 1)TRUE | 0.51 | 0.36 – 0.75 | <.001 | |
| cut2(PROPFULL_BEDS, c(0.9, 1)) | ||||
| cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) | 2.32 | 1.49 – 3.66 | <.001 | |
| cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] | 3.35 | 1.91 – 5.85 | <.001 | |
| relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”) | ||||
| relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[ 0.117, 7.000) | 1.21 | 0.36 – 3.08 | .721 | |
| relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[12.000,19.000) | 1.35 | 1.17 – 1.56 | <.001 | |
| relevel(cut2(hourofcallout2, c(7, 12, 19)), “[ 7.000,12.000)”)[19.000,23.867] | 1.10 | 0.64 – 1.79 | .709 | |
| as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) | 1.51 | 0.95 – 2.39 | .079 | |
| as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] | 2.83 | 1.62 – 4.98 | <.001 | |
| Observations | 9673 | |||
Answer 1:
Solid evidence: When Hospital is near or over capacity, when mrsa/cdiff have to be taken into account (more likely); When the callout is made during rounds or in more recent calendar year (less likely).
Less Solid Evidence: All of the above PLUS: On certain days of the week, sicker patients (OASIS), certain months, vre (more likely); Female (less likely). Effect modified of “first available bed” and propfull beds. E.g., when hospital is <90% used: “first available bed” => less likely to have a long delay, but effect is negated or reversed when hospitals are full.
Question 2: Do people who have long discharge delays (>24 hours) die more often?
- Marginal evidence in above table: 5.4% vs 6.8% (p=0.06), but we know these patients are generally sicker, more likely to have concerns about cdiff, vre, mrsa, etc.
We will build a model in a similar way as before, but add the long discharge delay (>24hrs) into a model for hospital mortality, retaining our exposure of interested (DD) throughout.
| variable_name | level | 0 | 1 | p | test |
|---|---|---|---|---|---|
| n | 9140 | 533 | |||
| micu (%) | 1 | 9140 (100.0) | 533 (100.0) | NA | |
| age (mean (sd)) | 63.36 (18.02) | 69.16 (15.63) | <0.001 | ||
| callout_month (%) | 1 | 734 ( 8.0) | 40 ( 7.5) | 0.485 | |
| 2 | 699 ( 7.6) | 39 ( 7.3) | |||
| 3 | 680 ( 7.4) | 53 ( 9.9) | |||
| 4 | 681 ( 7.5) | 45 ( 8.4) | |||
| 5 | 751 ( 8.2) | 43 ( 8.1) | |||
| 6 | 705 ( 7.7) | 40 ( 7.5) | |||
| 7 | 737 ( 8.1) | 42 ( 7.9) | |||
| 8 | 820 ( 9.0) | 57 ( 10.7) | |||
| 9 | 808 ( 8.8) | 43 ( 8.1) | |||
| 10 | 870 ( 9.5) | 44 ( 8.3) | |||
| 11 | 788 ( 8.6) | 35 ( 6.6) | |||
| 12 | 867 ( 9.5) | 52 ( 9.8) | |||
| female (%) | 0 | 4662 ( 51.0) | 294 ( 55.2) | 0.069 | |
| 1 | 4478 ( 49.0) | 239 ( 44.8) | |||
| request_tele (%) | 0 | 5943 ( 65.0) | 375 ( 70.4) | 0.014 | |
| 1 | 3197 ( 35.0) | 158 ( 29.6) | |||
| request_resp (%) | 0 | 8987 ( 98.3) | 527 ( 98.9) | 0.428 | |
| 1 | 153 ( 1.7) | 6 ( 1.1) | |||
| request_cdiff (%) | 0 | 8709 ( 95.3) | 481 ( 90.2) | <0.001 | |
| 1 | 431 ( 4.7) | 52 ( 9.8) | |||
| request_mrsa (%) | 0 | 7948 ( 87.0) | 457 ( 85.7) | 0.457 | |
| 1 | 1192 ( 13.0) | 76 ( 14.3) | |||
| request_vre (%) | 0 | 8704 ( 95.2) | 478 ( 89.7) | <0.001 | |
| 1 | 436 ( 4.8) | 55 ( 10.3) | |||
| oasis (mean (sd)) | 29.55 (7.14) | 34.79 (8.12) | <0.001 | ||
| elixhauser_hospital (mean (sd)) | 3.26 (7.20) | 8.38 (6.93) | <0.001 | ||
| ethnicity (%) | White | 6515 ( 71.3) | 395 ( 74.1) | 0.008 | |
| African American/Black | 1436 ( 15.7) | 58 ( 10.9) | |||
| Other | 1189 ( 13.0) | 80 ( 15.0) | |||
| MED_SERVICE (%) | FALSE | 659 ( 7.2) | 33 ( 6.2) | 0.423 | |
| TRUE | 8481 ( 92.8) | 500 ( 93.8) | |||
| HOSP_FREE_DAYS (median [IQR]) | 24.24 [21.23, 26.06] | 0.00 [0.00, 0.00] | <0.001 | nonnorm | |
| callout_dayofweek (%) | friday | 1368 ( 15.0) | 74 ( 13.9) | 0.249 | |
| monday | 1227 ( 13.4) | 73 ( 13.7) | |||
| saturday | 1229 ( 13.4) | 63 ( 11.8) | |||
| sunday | 1202 ( 13.2) | 65 ( 12.2) | |||
| thursday | 1326 ( 14.5) | 86 ( 16.1) | |||
| tuesday | 1326 ( 14.5) | 96 ( 18.0) | |||
| wednesday | 1462 ( 16.0) | 76 ( 14.3) | |||
| CALLOUT_DURING_NIGHT (%) | FALSE | 9081 ( 99.4) | 526 ( 98.7) | 0.121 | |
| TRUE | 59 ( 0.6) | 7 ( 1.3) | |||
| CALLOUT_DURING_ROUNDS (%) | FALSE | 3583 ( 39.2) | 233 ( 43.7) | 0.043 | |
| TRUE | 5557 ( 60.8) | 300 ( 56.3) | |||
| DISCHARGEDELAY_HOURS (mean (sd)) | 10.31 (10.16) | 11.47 (12.12) | 0.011 | ||
| hourofcallout2 (median [IQR]) | 11.38 [10.10, 13.15] | 11.60 [10.23, 13.52] | 0.076 | nonnorm | |
| PROPFULL_BEDS (mean (sd)) | 0.91 (0.09) | 0.91 (0.09) | 0.543 | ||
| postcalldaycat2 (%) | 0 | 7393 ( 80.9) | 407 ( 76.4) | 0.012 | |
| [1,5] | 1747 ( 19.1) | 126 ( 23.6) | |||
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.13] | 0.00 [0.00, 1.87] | <0.001 | nonnorm | |
| los_post_callout_days (median [IQR]) | 4.13 [2.27, 7.12] | 6.86 [2.87, 14.67] | <0.001 | nonnorm | |
| los_post_icu_days (median [IQR]) | 3.76 [1.94, 6.77] | 6.36 [2.59, 14.16] | <0.001 | nonnorm | |
| los_pre_callout_days (median [IQR]) | 1.74 [0.93, 3.55] | 3.48 [1.51, 7.67] | <0.001 | nonnorm | |
| callout_year (%) | 2005 | 346 ( 3.8) | 30 ( 5.6) | 0.015 | |
| 2006 | 996 ( 10.9) | 74 ( 13.9) | |||
| 2007 | 1329 ( 14.5) | 88 ( 16.5) | |||
| 2008 | 1561 ( 17.1) | 77 ( 14.4) | |||
| 2009 | 1586 ( 17.4) | 82 ( 15.4) | |||
| 2010 | 1639 ( 17.9) | 100 ( 18.8) | |||
| 2011 | 1683 ( 18.4) | 82 ( 15.4) |
## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3548.4
## cut2(oasis, g = 3) 2 3661.3
## cut2(age, g = 3) 2 3550.5
## female 1 3551.4
## request_tele 1 3558.1
## request_resp 1 3548.4
## request_mrsa 1 3548.7
## request_vre 1 3562.7
## request_cdiff 1 3554.0
## cut2(elixhauser_hospital, g = 3) 2 3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3649.1
## as.factor(callout_month) 11 3563.6
## as.factor(callout_year) 6 3566.8
## as.factor(callout_dayofweek) 6 3555.3
## MED_SERVICE 1 3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.5
## AIC
## <none> 3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3646.4
## cut2(oasis, g = 3) 3757.3
## cut2(age, g = 3) 3646.5
## female 3649.4
## request_tele 3656.1
## request_resp 3646.4
## request_mrsa 3646.7
## request_vre 3660.7
## request_cdiff 3652.0
## cut2(elixhauser_hospital, g = 3) 3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3741.1
## as.factor(callout_month) 3641.6
## as.factor(callout_year) 3654.8
## as.factor(callout_dayofweek) 3643.3
## MED_SERVICE 3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 3646.5
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 112.908
## cut2(age, g = 3) 2.067
## female 3.022
## request_tele 9.682
## request_resp 0.052
## request_mrsa 0.285
## request_vre 14.309
## request_cdiff 5.582
## cut2(elixhauser_hospital, g = 3) 100.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 100.720
## as.factor(callout_month) 15.184
## as.factor(callout_year) 18.444
## as.factor(callout_dayofweek) 6.953
## MED_SERVICE 2.846
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.720
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2.078
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9868110
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.3557399
## female 0.0821435
## request_tele 0.0018605
## request_resp 0.8204068
## request_mrsa 0.5931973
## request_vre 0.0001551
## request_cdiff 0.0181414
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1742241
## as.factor(callout_year) 0.0052129
## as.factor(callout_dayofweek) 0.3251854
## MED_SERVICE 0.0915849
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1260420
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.3538647
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## MED_SERVICE .
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + request_tele + request_vre + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_wardid ==
## 1)
## Df Deviance
## <none> 3607.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3608.5
## cut2(oasis, g = 3) 2 3746.4
## request_tele 1 3618.3
## request_vre 1 3620.3
## cut2(elixhauser_hospital, g = 3) 2 3727.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3706.6
## as.factor(callout_wardid == 1) 1 3636.2
## AIC LRT
## <none> 3633.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3632.5 0.878
## cut2(oasis, g = 3) 3768.4 138.690
## request_tele 3642.3 10.614
## request_vre 3644.3 12.661
## cut2(elixhauser_hospital, g = 3) 3749.4 119.730
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3724.6 98.922
## as.factor(callout_wardid == 1) 3660.2 28.563
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.3487705
## cut2(oasis, g = 3) < 2.2e-16 ***
## request_tele 0.0011223 **
## request_vre 0.0003733 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_wardid == 1) 9.071e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1) + MED_SERVICE
## Df Deviance
## <none> 3581.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3581.1
## cut2(oasis, g = 3) 2 3715.3
## female 1 3584.0
## request_tele 1 3590.2
## request_vre 1 3594.3
## request_cdiff 1 3587.5
## cut2(elixhauser_hospital, g = 3) 2 3689.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3679.1
## as.factor(callout_year) 6 3596.6
## as.factor(callout_wardid == 1) 1 3616.7
## MED_SERVICE 1 3583.6
## AIC LRT
## <none> 3625.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.1 0.021
## cut2(oasis, g = 3) 3755.3 134.221
## female 3626.0 2.899
## request_tele 3632.2 9.117
## request_vre 3636.3 13.194
## request_cdiff 3629.5 6.433
## cut2(elixhauser_hospital, g = 3) 3729.6 108.553
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3715.1 98.067
## as.factor(callout_year) 3628.6 15.491
## as.factor(callout_wardid == 1) 3658.7 35.666
## MED_SERVICE 3625.6 2.520
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8851019
## cut2(oasis, g = 3) < 2.2e-16 ***
## female 0.0886075 .
## request_tele 0.0025331 **
## request_vre 0.0002808 ***
## request_cdiff 0.0112050 *
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0167636 *
## as.factor(callout_wardid == 1) 2.342e-09 ***
## MED_SERVICE 0.1123962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## glm(formula = hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6299 -0.3551 -0.2382 -0.1573 3.2778
##
## Coefficients:
## Estimate
## (Intercept) -4.082023
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.002475
## cut2(oasis, g = 3)[27,34) 0.667113
## cut2(oasis, g = 3)[34,64] 1.381300
## cut2(age, g = 3)[56.1,73.8) 0.068712
## cut2(age, g = 3)[73.8,91.4] 0.178809
## female -0.164041
## request_tele -0.314127
## request_resp -0.096674
## request_mrsa -0.072664
## request_vre 0.660880
## request_cdiff 0.403258
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.717941
## cut2(elixhauser_hospital, g = 3)[ 7,31] 1.274143
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) -0.070743
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.523098
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.885316
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 2.440485
## as.factor(callout_month)2 0.043364
## as.factor(callout_month)3 0.292223
## as.factor(callout_month)4 0.167589
## as.factor(callout_month)5 0.041533
## as.factor(callout_month)6 0.045715
## as.factor(callout_month)7 0.050874
## as.factor(callout_month)8 0.257792
## as.factor(callout_month)9 -0.139124
## as.factor(callout_month)10 -0.176043
## as.factor(callout_month)11 -0.417376
## as.factor(callout_month)12 0.041012
## as.factor(callout_year)2006 -0.376682
## as.factor(callout_year)2007 -0.511288
## as.factor(callout_year)2008 -0.732877
## as.factor(callout_year)2009 -0.859038
## as.factor(callout_year)2010 -0.599114
## as.factor(callout_year)2011 -0.815269
## as.factor(callout_dayofweek)monday 0.036628
## as.factor(callout_dayofweek)saturday 0.040050
## as.factor(callout_dayofweek)sunday 0.036155
## as.factor(callout_dayofweek)thursday 0.153686
## as.factor(callout_dayofweek)tuesday 0.320468
## as.factor(callout_dayofweek)wednesday -0.077525
## as.factor(callout_wardid == 1)TRUE -0.530349
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.237551
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.204866
## MED_SERVICETRUE 0.336763
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.905215
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.092518
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.422993
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.348663
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.284574
## Std. Error
## (Intercept) 0.436454
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.149667
## cut2(oasis, g = 3)[27,34) 0.145238
## cut2(oasis, g = 3)[34,64] 0.142961
## cut2(age, g = 3)[56.1,73.8) 0.128484
## cut2(age, g = 3)[73.8,91.4] 0.129804
## female 0.094528
## request_tele 0.102492
## request_resp 0.431362
## request_mrsa 0.136850
## request_vre 0.165746
## request_cdiff 0.164761
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.151081
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.140125
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.136524
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.141507
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.148620
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.328256
## as.factor(callout_month)2 0.241413
## as.factor(callout_month)3 0.227627
## as.factor(callout_month)4 0.235199
## as.factor(callout_month)5 0.236091
## as.factor(callout_month)6 0.240511
## as.factor(callout_month)7 0.237562
## as.factor(callout_month)8 0.224792
## as.factor(callout_month)9 0.237640
## as.factor(callout_month)10 0.237104
## as.factor(callout_month)11 0.249786
## as.factor(callout_month)12 0.230016
## as.factor(callout_year)2006 0.244897
## as.factor(callout_year)2007 0.241150
## as.factor(callout_year)2008 0.245039
## as.factor(callout_year)2009 0.246656
## as.factor(callout_year)2010 0.240234
## as.factor(callout_year)2011 0.243189
## as.factor(callout_dayofweek)monday 0.178801
## as.factor(callout_dayofweek)saturday 0.196377
## as.factor(callout_dayofweek)sunday 0.194762
## as.factor(callout_dayofweek)thursday 0.176102
## as.factor(callout_dayofweek)tuesday 0.171798
## as.factor(callout_dayofweek)wednesday 0.182603
## as.factor(callout_wardid == 1)TRUE 0.192263
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.233908
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.311475
## MED_SERVICETRUE 0.206249
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.445336
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.097977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.289739
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.248295
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.329251
## z value
## (Intercept) -9.353
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.017
## cut2(oasis, g = 3)[27,34) 4.593
## cut2(oasis, g = 3)[34,64] 9.662
## cut2(age, g = 3)[56.1,73.8) 0.535
## cut2(age, g = 3)[73.8,91.4] 1.378
## female -1.735
## request_tele -3.065
## request_resp -0.224
## request_mrsa -0.531
## request_vre 3.987
## request_cdiff 2.448
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 4.752
## cut2(elixhauser_hospital, g = 3)[ 7,31] 9.093
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) -0.518
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 3.697
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 5.957
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 7.435
## as.factor(callout_month)2 0.180
## as.factor(callout_month)3 1.284
## as.factor(callout_month)4 0.713
## as.factor(callout_month)5 0.176
## as.factor(callout_month)6 0.190
## as.factor(callout_month)7 0.214
## as.factor(callout_month)8 1.147
## as.factor(callout_month)9 -0.585
## as.factor(callout_month)10 -0.742
## as.factor(callout_month)11 -1.671
## as.factor(callout_month)12 0.178
## as.factor(callout_year)2006 -1.538
## as.factor(callout_year)2007 -2.120
## as.factor(callout_year)2008 -2.991
## as.factor(callout_year)2009 -3.483
## as.factor(callout_year)2010 -2.494
## as.factor(callout_year)2011 -3.352
## as.factor(callout_dayofweek)monday 0.205
## as.factor(callout_dayofweek)saturday 0.204
## as.factor(callout_dayofweek)sunday 0.186
## as.factor(callout_dayofweek)thursday 0.873
## as.factor(callout_dayofweek)tuesday 1.865
## as.factor(callout_dayofweek)wednesday -0.425
## as.factor(callout_wardid == 1)TRUE -2.758
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 1.016
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.658
## MED_SERVICETRUE 1.633
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 2.033
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.944
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 1.460
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -1.404
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.864
## Pr(>|z|)
## (Intercept) < 2e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.986808
## cut2(oasis, g = 3)[27,34) 4.36e-06
## cut2(oasis, g = 3)[34,64] < 2e-16
## cut2(age, g = 3)[56.1,73.8) 0.592798
## cut2(age, g = 3)[73.8,91.4] 0.168347
## female 0.082676
## request_tele 0.002177
## request_resp 0.822668
## request_mrsa 0.595435
## request_vre 6.68e-05
## request_cdiff 0.014384
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 2.01e-06
## cut2(elixhauser_hospital, g = 3)[ 7,31] < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.604340
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.000218
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 2.57e-09
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 1.05e-13
## as.factor(callout_month)2 0.857448
## as.factor(callout_month)3 0.199218
## as.factor(callout_month)4 0.476128
## as.factor(callout_month)5 0.860357
## as.factor(callout_month)6 0.849251
## as.factor(callout_month)7 0.830431
## as.factor(callout_month)8 0.251463
## as.factor(callout_month)9 0.558252
## as.factor(callout_month)10 0.457800
## as.factor(callout_month)11 0.094734
## as.factor(callout_month)12 0.858487
## as.factor(callout_year)2006 0.124018
## as.factor(callout_year)2007 0.033989
## as.factor(callout_year)2008 0.002782
## as.factor(callout_year)2009 0.000496
## as.factor(callout_year)2010 0.012636
## as.factor(callout_year)2011 0.000801
## as.factor(callout_dayofweek)monday 0.837686
## as.factor(callout_dayofweek)saturday 0.838397
## as.factor(callout_dayofweek)sunday 0.852731
## as.factor(callout_dayofweek)thursday 0.382820
## as.factor(callout_dayofweek)tuesday 0.062128
## as.factor(callout_dayofweek)wednesday 0.671161
## as.factor(callout_wardid == 1)TRUE 0.005808
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.309834
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.510713
## MED_SERVICETRUE 0.102512
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.042087
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.345028
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.144314
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.160251
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.387420
##
## (Intercept) ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE
## cut2(oasis, g = 3)[27,34) ***
## cut2(oasis, g = 3)[34,64] ***
## cut2(age, g = 3)[56.1,73.8)
## cut2(age, g = 3)[73.8,91.4]
## female .
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3)[ 1, 7) ***
## cut2(elixhauser_hospital, g = 3)[ 7,31] ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000)
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] ***
## as.factor(callout_month)2
## as.factor(callout_month)3
## as.factor(callout_month)4
## as.factor(callout_month)5
## as.factor(callout_month)6
## as.factor(callout_month)7
## as.factor(callout_month)8
## as.factor(callout_month)9
## as.factor(callout_month)10
## as.factor(callout_month)11 .
## as.factor(callout_month)12
## as.factor(callout_year)2006
## as.factor(callout_year)2007 *
## as.factor(callout_year)2008 **
## as.factor(callout_year)2009 ***
## as.factor(callout_year)2010 *
## as.factor(callout_year)2011 ***
## as.factor(callout_dayofweek)monday
## as.factor(callout_dayofweek)saturday
## as.factor(callout_dayofweek)sunday
## as.factor(callout_dayofweek)thursday
## as.factor(callout_dayofweek)tuesday .
## as.factor(callout_dayofweek)wednesday
## as.factor(callout_wardid == 1)TRUE **
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## MED_SERVICETRUE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4126.0 on 9672 degrees of freedom
## Residual deviance: 3548.4 on 9623 degrees of freedom
## AIC: 3648.4
##
## Number of Fisher Scoring iterations: 6## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3548.4
## cut2(oasis, g = 3) 2 3661.3
## cut2(age, g = 3) 2 3550.5
## female 1 3551.4
## request_tele 1 3558.1
## request_resp 1 3548.4
## request_mrsa 1 3548.7
## request_vre 1 3562.7
## request_cdiff 1 3554.0
## cut2(elixhauser_hospital, g = 3) 2 3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3649.1
## as.factor(callout_month) 11 3563.6
## as.factor(callout_year) 6 3566.8
## as.factor(callout_dayofweek) 6 3555.3
## MED_SERVICE 1 3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.5
## AIC
## <none> 3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3646.4
## cut2(oasis, g = 3) 3757.3
## cut2(age, g = 3) 3646.5
## female 3649.4
## request_tele 3656.1
## request_resp 3646.4
## request_mrsa 3646.7
## request_vre 3660.7
## request_cdiff 3652.0
## cut2(elixhauser_hospital, g = 3) 3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3741.1
## as.factor(callout_month) 3641.6
## as.factor(callout_year) 3654.8
## as.factor(callout_dayofweek) 3643.3
## MED_SERVICE 3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 3646.5## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3548.4
## cut2(oasis, g = 3) 2 3661.3
## cut2(age, g = 3) 2 3550.5
## female 1 3551.4
## request_tele 1 3558.1
## request_resp 1 3548.4
## request_mrsa 1 3548.7
## request_vre 1 3562.7
## request_cdiff 1 3554.0
## cut2(elixhauser_hospital, g = 3) 2 3648.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3649.1
## as.factor(callout_month) 11 3563.6
## as.factor(callout_year) 6 3566.8
## as.factor(callout_dayofweek) 6 3555.3
## MED_SERVICE 1 3551.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3554.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.5
## AIC
## <none> 3648.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3646.4
## cut2(oasis, g = 3) 3757.3
## cut2(age, g = 3) 3646.5
## female 3649.4
## request_tele 3656.1
## request_resp 3646.4
## request_mrsa 3646.7
## request_vre 3660.7
## request_cdiff 3652.0
## cut2(elixhauser_hospital, g = 3) 3744.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3741.1
## as.factor(callout_month) 3641.6
## as.factor(callout_year) 3654.8
## as.factor(callout_dayofweek) 3643.3
## MED_SERVICE 3649.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3648.1
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 3646.5
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 112.908
## cut2(age, g = 3) 2.067
## female 3.022
## request_tele 9.682
## request_resp 0.052
## request_mrsa 0.285
## request_vre 14.309
## request_cdiff 5.582
## cut2(elixhauser_hospital, g = 3) 100.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 100.720
## as.factor(callout_month) 15.184
## as.factor(callout_year) 18.444
## as.factor(callout_dayofweek) 6.953
## MED_SERVICE 2.846
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.720
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2.078
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9868110
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.3557399
## female 0.0821435
## request_tele 0.0018605
## request_resp 0.8204068
## request_mrsa 0.5931973
## request_vre 0.0001551
## request_cdiff 0.0181414
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1742241
## as.factor(callout_year) 0.0052129
## as.factor(callout_dayofweek) 0.3251854
## MED_SERVICE 0.0915849
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1260420
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.3538647
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## MED_SERVICE .
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_resp"## [1] 0.8204068## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_mrsa + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS,
## c(0.9, 1))
## Df Deviance
## <none> 3548.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3548.4
## cut2(oasis, g = 3) 2 3661.4
## cut2(age, g = 3) 2 3550.5
## female 1 3551.5
## request_tele 1 3558.1
## request_mrsa 1 3548.7
## request_vre 1 3562.8
## request_cdiff 1 3554.0
## cut2(elixhauser_hospital, g = 3) 2 3648.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3649.4
## as.factor(callout_month) 11 3563.6
## as.factor(callout_year) 6 3567.0
## as.factor(callout_dayofweek) 6 3555.4
## MED_SERVICE 1 3551.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3554.2
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.5
## AIC
## <none> 3646.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3644.4
## cut2(oasis, g = 3) 3755.4
## cut2(age, g = 3) 3644.5
## female 3647.5
## request_tele 3654.1
## request_mrsa 3644.7
## request_vre 3658.8
## request_cdiff 3650.0
## cut2(elixhauser_hospital, g = 3) 3742.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3739.4
## as.factor(callout_month) 3639.6
## as.factor(callout_year) 3653.0
## as.factor(callout_dayofweek) 3641.4
## MED_SERVICE 3647.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3646.2
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 3644.5
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 112.917
## cut2(age, g = 3) 2.086
## female 3.013
## request_tele 9.675
## request_mrsa 0.290
## request_vre 14.327
## request_cdiff 5.575
## cut2(elixhauser_hospital, g = 3) 100.433
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 100.984
## as.factor(callout_month) 15.191
## as.factor(callout_year) 18.529
## as.factor(callout_dayofweek) 6.961
## MED_SERVICE 2.843
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.750
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2.099
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9851924
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.3524507
## female 0.0826076
## request_tele 0.0018683
## request_mrsa 0.5902019
## request_vre 0.0001536
## request_cdiff 0.0182146
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1739024
## as.factor(callout_year) 0.0050380
## as.factor(callout_dayofweek) 0.3244406
## MED_SERVICE 0.0917853
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1244301
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.3501837
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_mrsa
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## MED_SERVICE .
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_mrsa"## [1] 0.5902019## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS,
## c(0.9, 1))
## Df Deviance
## <none> 3548.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3548.7
## cut2(oasis, g = 3) 2 3661.5
## cut2(age, g = 3) 2 3550.8
## female 1 3551.7
## request_tele 1 3558.4
## request_vre 1 3562.8
## request_cdiff 1 3554.4
## cut2(elixhauser_hospital, g = 3) 2 3649.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3649.5
## as.factor(callout_month) 11 3564.0
## as.factor(callout_year) 6 3567.4
## as.factor(callout_dayofweek) 6 3555.7
## MED_SERVICE 1 3551.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3554.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.8
## AIC
## <none> 3644.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3642.7
## cut2(oasis, g = 3) 3753.5
## cut2(age, g = 3) 3642.8
## female 3645.7
## request_tele 3652.4
## request_vre 3656.8
## request_cdiff 3648.4
## cut2(elixhauser_hospital, g = 3) 3741.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3737.5
## as.factor(callout_month) 3638.0
## as.factor(callout_year) 3651.4
## as.factor(callout_dayofweek) 3639.7
## MED_SERVICE 3645.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3644.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 3642.8
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 112.800
## cut2(age, g = 3) 2.037
## female 3.007
## request_tele 9.698
## request_vre 14.040
## request_cdiff 5.635
## cut2(elixhauser_hospital, g = 3) 100.599
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 100.739
## as.factor(callout_month) 15.249
## as.factor(callout_year) 18.693
## as.factor(callout_dayofweek) 6.925
## MED_SERVICE 2.775
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.782
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2.090
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9945113
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.3611395
## female 0.0829036
## request_tele 0.0018452
## request_vre 0.0001789
## request_cdiff 0.0176010
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1713729
## as.factor(callout_year) 0.0047149
## as.factor(callout_dayofweek) 0.3278483
## MED_SERVICE 0.0957744
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1227164
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.3517602
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## MED_SERVICE .
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(age, g = 3)"## [1] 0.3611395## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3550.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3550.8
## cut2(oasis, g = 3) 2 3663.5
## cut2(age, g = 3) 2 3552.8
## female 1 3553.7
## request_tele 1 3560.6
## request_vre 1 3564.8
## request_cdiff 1 3556.7
## cut2(elixhauser_hospital, g = 3) 2 3652.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3650.8
## as.factor(callout_month) 11 3565.9
## as.factor(callout_year) 6 3569.6
## as.factor(callout_dayofweek) 6 3557.7
## as.factor(callout_wardid == 1) 1 3587.5
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3550.9
## MED_SERVICE 1 3553.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3556.3
## AIC
## <none> 3642.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3640.8
## cut2(oasis, g = 3) 3751.5
## cut2(age, g = 3) 3640.8
## female 3643.7
## request_tele 3650.6
## request_vre 3654.8
## request_cdiff 3646.7
## cut2(elixhauser_hospital, g = 3) 3740.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3734.8
## as.factor(callout_month) 3635.9
## as.factor(callout_year) 3649.6
## as.factor(callout_dayofweek) 3637.7
## as.factor(callout_wardid == 1) 3677.5
## cut2(PROPFULL_BEDS, c(0.9, 1)) 3638.9
## MED_SERVICE 3643.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3642.3
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.001
## cut2(oasis, g = 3) 112.726
## cut2(age, g = 3) 1.964
## female 2.890
## request_tele 9.764
## request_vre 14.009
## request_cdiff 5.898
## cut2(elixhauser_hospital, g = 3) 101.574
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 99.934
## as.factor(callout_month) 15.079
## as.factor(callout_year) 18.775
## as.factor(callout_dayofweek) 6.905
## as.factor(callout_wardid == 1) 36.655
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.048
## MED_SERVICE 2.680
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.461
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9736030
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.3746428
## female 0.0891327
## request_tele 0.0017800
## request_vre 0.0001819
## request_cdiff 0.0151560
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1788982
## as.factor(callout_year) 0.0045604
## as.factor(callout_dayofweek) 0.3297315
## as.factor(callout_wardid == 1) 1.41e-09
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.9761058
## MED_SERVICE 0.1016153
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1410098
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.9761058## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + cut2(age, g = 3) + female + request_tele +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3550.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3550.9
## cut2(oasis, g = 3) 2 3663.7
## cut2(age, g = 3) 2 3552.8
## female 1 3553.8
## request_tele 1 3560.7
## request_vre 1 3564.9
## request_cdiff 1 3556.8
## cut2(elixhauser_hospital, g = 3) 2 3652.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3651.0
## as.factor(callout_month) 11 3565.9
## as.factor(callout_year) 6 3569.7
## as.factor(callout_dayofweek) 6 3557.8
## as.factor(callout_wardid == 1) 1 3587.5
## MED_SERVICE 1 3553.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3556.4
## AIC
## <none> 3638.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3636.9
## cut2(oasis, g = 3) 3747.7
## cut2(age, g = 3) 3636.8
## female 3639.8
## request_tele 3646.7
## request_vre 3650.9
## request_cdiff 3642.8
## cut2(elixhauser_hospital, g = 3) 3736.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3731.0
## as.factor(callout_month) 3631.9
## as.factor(callout_year) 3645.7
## as.factor(callout_dayofweek) 3633.8
## as.factor(callout_wardid == 1) 3673.5
## MED_SERVICE 3639.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3638.4
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.002
## cut2(oasis, g = 3) 112.818
## cut2(age, g = 3) 1.971
## female 2.904
## request_tele 9.834
## request_vre 14.061
## request_cdiff 5.925
## cut2(elixhauser_hospital, g = 3) 101.607
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 100.112
## as.factor(callout_month) 15.046
## as.factor(callout_year) 18.788
## as.factor(callout_dayofweek) 6.926
## as.factor(callout_wardid == 1) 36.657
## MED_SERVICE 2.687
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.496
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.964406
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.373183
## female 0.088365
## request_tele 0.001713
## request_vre 0.000177
## request_cdiff 0.014926
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.180405
## as.factor(callout_year) 0.004537
## as.factor(callout_dayofweek) 0.327704
## as.factor(callout_wardid == 1) 1.409e-09
## MED_SERVICE 0.101144
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.138867
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3)
## female .
## request_tele **
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(age, g = 3)"## [1] 0.3731832## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3552.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3552.8
## cut2(oasis, g = 3) 2 3687.3
## female 1 3555.5
## request_tele 1 3562.0
## request_vre 1 3566.6
## request_cdiff 1 3559.1
## cut2(elixhauser_hospital, g = 3) 2 3662.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3651.1
## as.factor(callout_month) 11 3568.1
## as.factor(callout_year) 6 3571.5
## as.factor(callout_dayofweek) 6 3559.8
## as.factor(callout_wardid == 1) 1 3588.1
## MED_SERVICE 1 3555.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3558.2
## AIC
## <none> 3636.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3634.8
## cut2(oasis, g = 3) 3767.3
## female 3637.5
## request_tele 3644.0
## request_vre 3648.6
## request_cdiff 3641.1
## cut2(elixhauser_hospital, g = 3) 3742.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3727.1
## as.factor(callout_month) 3630.1
## as.factor(callout_year) 3643.5
## as.factor(callout_dayofweek) 3631.8
## as.factor(callout_wardid == 1) 3670.1
## MED_SERVICE 3637.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3636.2
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.006
## cut2(oasis, g = 3) 134.412
## female 2.674
## request_tele 9.205
## request_vre 13.798
## request_cdiff 6.279
## cut2(elixhauser_hospital, g = 3) 109.439
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 98.254
## as.factor(callout_month) 15.218
## as.factor(callout_year) 18.705
## as.factor(callout_dayofweek) 6.926
## as.factor(callout_wardid == 1) 35.263
## MED_SERVICE 2.463
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.377
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9407325
## cut2(oasis, g = 3) < 2.2e-16
## female 0.1020267
## request_tele 0.0024130
## request_vre 0.0002035
## request_cdiff 0.0122157
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.1727489
## as.factor(callout_year) 0.0046927
## as.factor(callout_dayofweek) 0.3277516
## as.factor(callout_wardid == 1) 2.88e-09
## MED_SERVICE 0.1165784
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1461829
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## female
## request_tele **
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_dayofweek)"## [1] 0.3277516## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3559.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3559.8
## cut2(oasis, g = 3) 2 3694.3
## female 1 3562.5
## request_tele 1 3568.8
## request_vre 1 3573.5
## request_cdiff 1 3566.1
## cut2(elixhauser_hospital, g = 3) 2 3669.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3657.8
## as.factor(callout_month) 11 3575.1
## as.factor(callout_year) 6 3578.8
## as.factor(callout_wardid == 1) 1 3594.8
## MED_SERVICE 1 3562.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3565.4
## AIC
## <none> 3631.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3629.8
## cut2(oasis, g = 3) 3762.3
## female 3632.5
## request_tele 3638.8
## request_vre 3643.5
## request_cdiff 3636.1
## cut2(elixhauser_hospital, g = 3) 3737.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3721.8
## as.factor(callout_month) 3625.1
## as.factor(callout_year) 3638.8
## as.factor(callout_wardid == 1) 3664.8
## MED_SERVICE 3632.0
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3631.4
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.001
## cut2(oasis, g = 3) 134.520
## female 2.710
## request_tele 9.067
## request_vre 13.730
## request_cdiff 6.293
## cut2(elixhauser_hospital, g = 3) 109.429
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 98.038
## as.factor(callout_month) 15.331
## as.factor(callout_year) 18.992
## as.factor(callout_wardid == 1) 35.029
## MED_SERVICE 2.280
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.629
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.978514
## cut2(oasis, g = 3) < 2.2e-16
## female 0.099728
## request_tele 0.002603
## request_vre 0.000211
## request_cdiff 0.012120
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.167834
## as.factor(callout_year) 0.004177
## as.factor(callout_wardid == 1) 3.249e-09
## MED_SERVICE 0.131081
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.131151
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## female .
## request_tele **
## request_vre ***
## request_cdiff *
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_month)"## [1] 0.167834## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3575.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3575.1
## cut2(oasis, g = 3) 2 3708.3
## female 1 3577.9
## request_tele 1 3584.4
## request_vre 1 3588.0
## request_cdiff 1 3581.8
## cut2(elixhauser_hospital, g = 3) 2 3683.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3672.7
## as.factor(callout_year) 6 3591.4
## as.factor(callout_wardid == 1) 1 3611.0
## MED_SERVICE 1 3577.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3581.1
## AIC
## <none> 3625.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.1
## cut2(oasis, g = 3) 3754.3
## female 3625.9
## request_tele 3632.4
## request_vre 3636.0
## request_cdiff 3629.8
## cut2(elixhauser_hospital, g = 3) 3729.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3714.7
## as.factor(callout_year) 3629.4
## as.factor(callout_wardid == 1) 3659.0
## MED_SERVICE 3625.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3625.1
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.040
## cut2(oasis, g = 3) 133.185
## female 2.816
## request_tele 9.266
## request_vre 12.893
## request_cdiff 6.656
## cut2(elixhauser_hospital, g = 3) 108.500
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 97.573
## as.factor(callout_year) 16.343
## as.factor(callout_wardid == 1) 35.911
## MED_SERVICE 2.502
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.977
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8406641
## cut2(oasis, g = 3) < 2.2e-16
## female 0.0933013
## request_tele 0.0023341
## request_vre 0.0003297
## request_cdiff 0.0098843
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.0120256
## as.factor(callout_wardid == 1) 2.066e-09
## MED_SERVICE 0.1136967
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1127364
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## female .
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) *
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "MED_SERVICE"## [1] 0.1136967## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 3577.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3577.6
## cut2(oasis, g = 3) 2 3714.3
## female 1 3580.5
## request_tele 1 3587.0
## request_vre 1 3590.7
## request_cdiff 1 3584.2
## cut2(elixhauser_hospital, g = 3) 2 3688.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3673.1
## as.factor(callout_year) 6 3594.4
## as.factor(callout_wardid == 1) 1 3611.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 3583.6
## AIC
## <none> 3625.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.6
## cut2(oasis, g = 3) 3758.3
## female 3626.5
## request_tele 3633.0
## request_vre 3636.7
## request_cdiff 3630.2
## cut2(elixhauser_hospital, g = 3) 3732.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3713.1
## as.factor(callout_year) 3630.4
## as.factor(callout_wardid == 1) 3657.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3625.6
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.035
## cut2(oasis, g = 3) 136.731
## female 2.927
## request_tele 9.378
## request_vre 13.049
## request_cdiff 6.645
## cut2(elixhauser_hospital, g = 3) 110.350
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 95.537
## as.factor(callout_year) 16.830
## as.factor(callout_wardid == 1) 33.555
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5.995
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8509339
## cut2(oasis, g = 3) < 2.2e-16
## female 0.0871324
## request_tele 0.0021962
## request_vre 0.0003034
## request_cdiff 0.0099458
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.0099270
## as.factor(callout_wardid == 1) 6.927e-09
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1118491
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## female .
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"## [1] 0.1118491## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + female + request_tele + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1)
## Df Deviance
## <none> 3583.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3583.6
## cut2(oasis, g = 3) 2 3721.5
## female 1 3586.6
## request_tele 1 3592.8
## request_vre 1 3597.0
## request_cdiff 1 3590.0
## cut2(elixhauser_hospital, g = 3) 2 3694.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3679.6
## as.factor(callout_year) 6 3599.5
## as.factor(callout_wardid == 1) 1 3616.9
## AIC LRT
## <none> 3625.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3623.6 0.017
## cut2(oasis, g = 3) 3759.5 137.900
## female 3626.6 3.008
## request_tele 3632.8 9.228
## request_vre 3637.0 13.370
## request_cdiff 3630.0 6.408
## cut2(elixhauser_hospital, g = 3) 3732.0 110.384
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3713.6 95.997
## as.factor(callout_year) 3629.5 15.950
## as.factor(callout_wardid == 1) 3656.9 33.293
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8960330
## cut2(oasis, g = 3) < 2.2e-16 ***
## female 0.0828467 .
## request_tele 0.0023833 **
## request_vre 0.0002557 ***
## request_cdiff 0.0113599 *
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0140253 *
## as.factor(callout_wardid == 1) 7.928e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "female"## [1] 0.08284666## Single term deletions
##
## Model:
## hospitaldeath ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") +
## cut2(oasis, g = 3) + request_tele + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1)
## Df Deviance
## <none> 3586.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 3586.6
## cut2(oasis, g = 3) 2 3723.0
## request_tele 1 3596.0
## request_vre 1 3599.6
## request_cdiff 1 3592.9
## cut2(elixhauser_hospital, g = 3) 2 3700.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 3682.2
## as.factor(callout_year) 6 3602.3
## as.factor(callout_wardid == 1) 1 3619.9
## AIC LRT
## <none> 3626.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3624.6 0.004
## cut2(oasis, g = 3) 3759.0 136.401
## request_tele 3634.0 9.429
## request_vre 3637.6 13.006
## request_cdiff 3630.9 6.296
## cut2(elixhauser_hospital, g = 3) 3736.3 113.710
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 3714.2 95.582
## as.factor(callout_year) 3630.3 15.648
## as.factor(callout_wardid == 1) 3657.9 33.251
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9477193
## cut2(oasis, g = 3) < 2.2e-16 ***
## request_tele 0.0021362 **
## request_vre 0.0003105 ***
## request_cdiff 0.0121009 *
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0157712 *
## as.factor(callout_wardid == 1) 8.101e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_year)"## [1] 0.01577116
| hospitaldeath | ||||
| Odds Ratio | CI | p | ||
| (Intercept) | 0.02 | 0.01 – 0.04 | <.001 | |
| I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”) | 0.99 | 0.74 – 1.31 | .948 | |
| cut2(oasis, g = 3) | ||||
| [27,34) | 2.03 | 1.54 – 2.71 | <.001 | |
| [34,64] | 4.20 | 3.23 – 5.52 | <.001 | |
| request_tele | 0.74 | 0.60 – 0.90 | .002 | |
| request_vre | 1.85 | 1.33 – 2.51 | <.001 | |
| request_cdiff | 1.53 | 1.10 – 2.09 | .009 | |
| cut2(elixhauser_hospital, g = 3) | ||||
| [ 1, 7) | 2.14 | 1.60 – 2.89 | <.001 | |
| [ 7,31] | 3.76 | 2.89 – 4.96 | <.001 | |
| cut2(los_pre_callout_days, c(1, 3, 7, 28)) | ||||
| [ 1.000, 3.000) | 0.92 | 0.71 – 1.21 | .550 | |
| [ 3.000, 7.000) | 1.65 | 1.25 – 2.17 | <.001 | |
| [ 7.000, 28.000) | 2.31 | 1.74 – 3.09 | <.001 | |
| [ 28.000,130.762] | 10.14 | 5.32 – 18.99 | <.001 | |
| as.factor(callout_year) | ||||
| 2006 | 0.79 | 0.50 – 1.27 | .320 | |
| 2007 | 0.70 | 0.45 – 1.12 | .128 | |
| 2008 | 0.55 | 0.35 – 0.88 | .010 | |
| 2009 | 0.50 | 0.32 – 0.80 | .003 | |
| 2010 | 0.64 | 0.41 – 1.01 | .049 | |
| 2011 | 0.52 | 0.33 – 0.83 | .005 | |
| as.factor(callout_wardid == 1) (TRUE) | 0.51 | 0.41 – 0.63 | <.001 | |
| Observations | 9673 | |||
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.991
## cut2(oasis, g = 3)[27,34) 2.034
## cut2(oasis, g = 3)[34,64] 4.199
## request_tele 0.736
## request_vre 1.848
## request_cdiff 1.531
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 2.142
## cut2(elixhauser_hospital, g = 3)[ 7,31] 3.759
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.922
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 1.646
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 2.314
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 10.140
## as.factor(callout_year)2006 0.790
## as.factor(callout_year)2007 0.704
## as.factor(callout_year)2008 0.547
## as.factor(callout_year)2009 0.501
## as.factor(callout_year)2010 0.638
## as.factor(callout_year)2011 0.520
## as.factor(callout_wardid == 1)TRUE 0.506
## 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.740
## cut2(oasis, g = 3)[27,34) 1.542
## cut2(oasis, g = 3)[34,64] 3.230
## request_tele 0.601
## request_vre 1.335
## request_cdiff 1.101
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 1.604
## cut2(elixhauser_hospital, g = 3)[ 7,31] 2.888
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.709
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 1.252
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 1.739
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 5.319
## as.factor(callout_year)2006 0.502
## as.factor(callout_year)2007 0.453
## as.factor(callout_year)2008 0.349
## as.factor(callout_year)2009 0.320
## as.factor(callout_year)2010 0.412
## as.factor(callout_year)2011 0.332
## as.factor(callout_wardid == 1)TRUE 0.406
## 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 1.309
## cut2(oasis, g = 3)[27,34) 2.705
## cut2(oasis, g = 3)[34,64] 5.520
## request_tele 0.896
## request_vre 2.514
## request_cdiff 2.091
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 2.887
## cut2(elixhauser_hospital, g = 3)[ 7,31] 4.956
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 1.206
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 2.170
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 3.087
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 18.993
## as.factor(callout_year)2006 1.272
## as.factor(callout_year)2007 1.121
## as.factor(callout_year)2008 0.877
## as.factor(callout_year)2009 0.803
## as.factor(callout_year)2010 1.013
## as.factor(callout_year)2011 0.834
## as.factor(callout_wardid == 1)TRUE 0.634
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.948
## cut2(oasis, g = 3)[27,34) 0.000
## cut2(oasis, g = 3)[34,64] 0.000
## request_tele 0.002
## request_vre 0.000
## request_cdiff 0.009
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.000
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.550
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.000
## as.factor(callout_year)2006 0.320
## as.factor(callout_year)2007 0.128
## as.factor(callout_year)2008 0.010
## as.factor(callout_year)2009 0.003
## as.factor(callout_year)2010 0.049
## as.factor(callout_year)2011 0.005
## as.factor(callout_wardid == 1)TRUE 0.000Answer 2: After adjusting for potential confounders, there is no statistically significant evidence that a long delay produces a better hospital mortality outcome.
Question 3: Do individuals with a long discharge delay have smaller numbers of hospital free days?
HFDs are technically ordinal, so we tried Proportional Odds Logistic Regression. This didn’t make it into the paper.
There is not very much evidence in the above table that hospital free days is impacted by long discharge delays (21.36 vs 21.09, p=0.265)
We can look at it using an empirical cumulative distribution function: 
Question 4: Do people who have long discharge delays (>24 hours) have a “good outcome” defined as a short post discharge LOS (<1 week) and survive?
##
## FALSE TRUE
## 2583 7090##
## FALSE TRUE
## 8662 1011##
## FALSE TRUE
## FALSE 2282 301
## TRUE 6380 710##
## FALSE TRUE
## FALSE 0.2634495 0.2977250
## TRUE 0.7365505 0.7022750##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: with(d, table(HOSP_FREE_DAYS > 21, cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]"))
## X-squared = 5.2604, df = 1, p-value = 0.02182| variable_name | level | FALSE | TRUE | p | test |
|---|---|---|---|---|---|
| n | 2583 | 7090 | |||
| micu (%) | 1 | 2583 (100.0) | 7090 (100.0) | NA | |
| age (mean (sd)) | 64.83 (16.45) | 63.26 (18.44) | <0.001 | ||
| callout_month (%) | 1 | 202 ( 7.8) | 572 ( 8.1) | 0.783 | |
| 2 | 198 ( 7.7) | 540 ( 7.6) | |||
| 3 | 191 ( 7.4) | 542 ( 7.6) | |||
| 4 | 219 ( 8.5) | 507 ( 7.2) | |||
| 5 | 210 ( 8.1) | 584 ( 8.2) | |||
| 6 | 199 ( 7.7) | 546 ( 7.7) | |||
| 7 | 207 ( 8.0) | 572 ( 8.1) | |||
| 8 | 231 ( 8.9) | 646 ( 9.1) | |||
| 9 | 240 ( 9.3) | 611 ( 8.6) | |||
| 10 | 241 ( 9.3) | 673 ( 9.5) | |||
| 11 | 207 ( 8.0) | 616 ( 8.7) | |||
| 12 | 238 ( 9.2) | 681 ( 9.6) | |||
| female (%) | 0 | 1353 ( 52.4) | 3603 ( 50.8) | 0.181 | |
| 1 | 1230 ( 47.6) | 3487 ( 49.2) | |||
| request_tele (%) | 0 | 1674 ( 64.8) | 4644 ( 65.5) | 0.543 | |
| 1 | 909 ( 35.2) | 2446 ( 34.5) | |||
| request_resp (%) | 0 | 2538 ( 98.3) | 6976 ( 98.4) | 0.712 | |
| 1 | 45 ( 1.7) | 114 ( 1.6) | |||
| request_cdiff (%) | 0 | 2402 ( 93.0) | 6788 ( 95.7) | <0.001 | |
| 1 | 181 ( 7.0) | 302 ( 4.3) | |||
| request_mrsa (%) | 0 | 2193 ( 84.9) | 6212 ( 87.6) | 0.001 | |
| 1 | 390 ( 15.1) | 878 ( 12.4) | |||
| request_vre (%) | 0 | 2377 ( 92.0) | 6805 ( 96.0) | <0.001 | |
| 1 | 206 ( 8.0) | 285 ( 4.0) | |||
| oasis (mean (sd)) | 31.12 (7.57) | 29.37 (7.14) | <0.001 | ||
| elixhauser_hospital (mean (sd)) | 5.58 (7.46) | 2.80 (7.06) | <0.001 | ||
| ethnicity (%) | White | 1857 ( 71.9) | 5053 ( 71.3) | 0.201 | |
| African American/Black | 373 ( 14.4) | 1121 ( 15.8) | |||
| Other | 353 ( 13.7) | 916 ( 12.9) | |||
| MED_SERVICE (%) | FALSE | 208 ( 8.1) | 484 ( 6.8) | 0.043 | |
| TRUE | 2375 ( 91.9) | 6606 ( 93.2) | |||
| HOSP_FREE_DAYS (mean (sd)) | 11.42 (8.08) | 24.95 (1.79) | <0.001 | ||
| callout_dayofweek (%) | friday | 382 ( 14.8) | 1060 ( 15.0) | 0.324 | |
| monday | 363 ( 14.1) | 937 ( 13.2) | |||
| saturday | 312 ( 12.1) | 980 ( 13.8) | |||
| sunday | 345 ( 13.4) | 922 ( 13.0) | |||
| thursday | 374 ( 14.5) | 1038 ( 14.6) | |||
| tuesday | 376 ( 14.6) | 1046 ( 14.8) | |||
| wednesday | 431 ( 16.7) | 1107 ( 15.6) | |||
| CALLOUT_DURING_NIGHT (%) | FALSE | 2560 ( 99.1) | 7047 ( 99.4) | 0.173 | |
| TRUE | 23 ( 0.9) | 43 ( 0.6) | |||
| CALLOUT_DURING_ROUNDS (%) | FALSE | 1070 ( 41.4) | 2746 ( 38.7) | 0.018 | |
| TRUE | 1513 ( 58.6) | 4344 ( 61.3) | |||
| DISCHARGEDELAY_HOURS (mean (sd)) | 10.71 (10.75) | 10.25 (10.10) | 0.053 | ||
| hourofcallout2 (median [IQR]) | 11.53 [10.27, 13.25] | 11.35 [10.05, 13.13] | <0.001 | nonnorm | |
| PROPFULL_BEDS (mean (sd)) | 0.91 (0.09) | 0.91 (0.09) | 0.677 | ||
| postcalldaycat2 (%) | 0 | 2045 ( 79.2) | 5755 ( 81.2) | 0.030 | |
| [1,5] | 538 ( 20.8) | 1335 ( 18.8) | |||
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.82] | 0.00 [0.00, 0.07] | <0.001 | nonnorm | |
| los_post_callout_days (median [IQR]) | 11.25 [8.36, 16.27] | 3.21 [2.14, 4.99] | <0.001 | nonnorm | |
| los_post_icu_days (median [IQR]) | 10.89 [8.00, 15.92] | 2.85 [1.79, 4.25] | <0.001 | nonnorm | |
| los_pre_callout_days (median [IQR]) | 2.73 [1.42, 6.49] | 1.64 [0.87, 2.97] | <0.001 | nonnorm | |
| callout_year (%) | 2005 | 114 ( 4.4) | 262 ( 3.7) | 0.001 | |
| 2006 | 329 ( 12.7) | 741 ( 10.5) | |||
| 2007 | 405 ( 15.7) | 1012 ( 14.3) | |||
| 2008 | 409 ( 15.8) | 1229 ( 17.3) | |||
| 2009 | 420 ( 16.3) | 1248 ( 17.6) | |||
| 2010 | 474 ( 18.4) | 1265 ( 17.8) | |||
| 2011 | 432 ( 16.7) | 1333 ( 18.8) | |||
| hospitaldeath (mean (sd)) | 0.21 (0.40) | 0.00 (0.00) | <0.001 |
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10335
## cut2(oasis, g = 3) 2 10371
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_tele 1 10336
## request_resp 1 10338
## request_mrsa 1 10337
## request_vre 1 10365
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10683
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## MED_SERVICE 1 10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10335
## AIC
## <none> 10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10433
## cut2(oasis, g = 3) 10467
## cut2(age, g = 3) 10438
## female 10434
## request_tele 10434
## request_resp 10436
## request_mrsa 10435
## request_vre 10463
## request_cdiff 10444
## cut2(elixhauser_hospital, g = 3) 10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10775
## as.factor(callout_month) 10427
## as.factor(callout_year) 10452
## as.factor(callout_dayofweek) 10428
## MED_SERVICE 10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 10431
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.10
## cut2(oasis, g = 3) 35.86
## cut2(age, g = 3) 6.90
## female 0.52
## request_tele 0.19
## request_resp 3.17
## request_mrsa 2.08
## request_vre 30.09
## request_cdiff 10.58
## cut2(elixhauser_hospital, g = 3) 131.80
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 348.11
## as.factor(callout_month) 13.82
## as.factor(callout_year) 29.07
## as.factor(callout_dayofweek) 4.64
## MED_SERVICE 0.02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.52
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.12
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.755910
## cut2(oasis, g = 3) 1.637e-08
## cut2(age, g = 3) 0.031693
## female 0.471577
## request_tele 0.658857
## request_resp 0.074821
## request_mrsa 0.149049
## request_vre 4.124e-08
## request_cdiff 0.001141
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.242854
## as.factor(callout_year) 5.909e-05
## as.factor(callout_dayofweek) 0.591136
## MED_SERVICE 0.877728
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.210127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.942011
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_tele
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_vre +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_wardid == 1)
## Df Deviance
## <none> 10404
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10407
## cut2(oasis, g = 3) 2 10438
## request_vre 1 10441
## cut2(elixhauser_hospital, g = 3) 2 10542
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10775
## as.factor(callout_wardid == 1) 1 10466
## AIC LRT
## <none> 10428
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10429 2.27
## cut2(oasis, g = 3) 10458 33.33
## request_vre 10463 36.09
## cut2(elixhauser_hospital, g = 3) 10562 137.49
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10791 370.45
## as.factor(callout_wardid == 1) 10488 61.52
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.1323
## cut2(oasis, g = 3) 5.779e-08 ***
## request_vre 1.880e-09 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_wardid == 1) 4.390e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1)
## Df Deviance
## <none> 10360
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10360
## cut2(oasis, g = 3) 2 10396
## cut2(age, g = 3) 2 10367
## request_resp 1 10362
## request_mrsa 1 10362
## request_vre 1 10390
## request_cdiff 1 10370
## cut2(elixhauser_hospital, g = 3) 2 10494
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10710
## as.factor(callout_year) 6 10384
## as.factor(callout_wardid == 1) 1 10426
## AIC LRT
## <none> 10406
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404 0.01
## cut2(oasis, g = 3) 10438 36.04
## cut2(age, g = 3) 10409 7.02
## request_resp 10406 2.57
## request_mrsa 10406 2.16
## request_vre 10434 30.78
## request_cdiff 10414 10.60
## cut2(elixhauser_hospital, g = 3) 10536 134.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10748 350.35
## as.factor(callout_year) 10418 24.90
## as.factor(callout_wardid == 1) 10470 65.97
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9128998
## cut2(oasis, g = 3) 1.489e-08 ***
## cut2(age, g = 3) 0.0298308 *
## request_resp 0.1089144
## request_mrsa 0.1416814
## request_vre 2.884e-08 ***
## request_cdiff 0.0011290 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0003568 ***
## as.factor(callout_wardid == 1) 4.580e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## glm(formula = I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS,
## c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7,
## 12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1794 -0.9757 0.6086 0.7738 1.9107
##
## Coefficients:
## Estimate
## (Intercept) 1.242e+00
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -2.538e-02
## cut2(oasis, g = 3)[27,34) -1.931e-01
## cut2(oasis, g = 3)[34,64] -3.924e-01
## cut2(age, g = 3)[56.1,73.8) -2.386e-02
## cut2(age, g = 3)[73.8,91.4] 1.293e-01
## female 3.514e-02
## request_tele -2.267e-02
## request_resp -3.399e-01
## request_mrsa -1.028e-01
## request_vre -5.711e-01
## request_cdiff -3.396e-01
## cut2(elixhauser_hospital, g = 3)[ 1, 7) -3.731e-01
## cut2(elixhauser_hospital, g = 3)[ 7,31] -7.008e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) -1.586e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) -6.209e-01
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) -1.314e+00
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] -2.088e+00
## as.factor(callout_month)2 -4.175e-02
## as.factor(callout_month)3 7.850e-02
## as.factor(callout_month)4 -1.967e-01
## as.factor(callout_month)5 4.161e-02
## as.factor(callout_month)6 -3.685e-03
## as.factor(callout_month)7 2.546e-02
## as.factor(callout_month)8 7.958e-06
## as.factor(callout_month)9 -5.039e-02
## as.factor(callout_month)10 5.643e-02
## as.factor(callout_month)11 2.117e-01
## as.factor(callout_month)12 7.454e-02
## as.factor(callout_year)2006 2.643e-02
## as.factor(callout_year)2007 1.661e-01
## as.factor(callout_year)2008 3.300e-01
## as.factor(callout_year)2009 4.001e-01
## as.factor(callout_year)2010 2.483e-01
## as.factor(callout_year)2011 4.167e-01
## as.factor(callout_dayofweek)monday -8.049e-02
## as.factor(callout_dayofweek)saturday 8.648e-02
## as.factor(callout_dayofweek)sunday -6.593e-03
## as.factor(callout_dayofweek)thursday -6.554e-03
## as.factor(callout_dayofweek)tuesday -2.928e-02
## as.factor(callout_dayofweek)wednesday -1.125e-01
## as.factor(callout_wardid == 1)TRUE 5.195e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 4.825e-02
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.842e-01
## MED_SERVICETRUE 1.481e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) -5.123e-01
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) -5.835e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] -1.137e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 3.136e-02
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -2.819e-02
## Std. Error
## (Intercept) 2.200e-01
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 8.155e-02
## cut2(oasis, g = 3)[27,34) 6.049e-02
## cut2(oasis, g = 3)[34,64] 6.559e-02
## cut2(age, g = 3)[56.1,73.8) 6.110e-02
## cut2(age, g = 3)[73.8,91.4] 6.578e-02
## female 4.881e-02
## request_tele 5.134e-02
## request_resp 1.867e-01
## request_mrsa 7.092e-02
## request_vre 1.025e-01
## request_cdiff 1.031e-01
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 6.284e-02
## cut2(elixhauser_hospital, g = 3)[ 7,31] 6.154e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 6.448e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 7.319e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 8.237e-02
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 3.237e-01
## as.factor(callout_month)2 1.229e-01
## as.factor(callout_month)3 1.241e-01
## as.factor(callout_month)4 1.213e-01
## as.factor(callout_month)5 1.212e-01
## as.factor(callout_month)6 1.228e-01
## as.factor(callout_month)7 1.215e-01
## as.factor(callout_month)8 1.187e-01
## as.factor(callout_month)9 1.188e-01
## as.factor(callout_month)10 1.182e-01
## as.factor(callout_month)11 1.221e-01
## as.factor(callout_month)12 1.193e-01
## as.factor(callout_year)2006 1.400e-01
## as.factor(callout_year)2007 1.374e-01
## as.factor(callout_year)2008 1.367e-01
## as.factor(callout_year)2009 1.374e-01
## as.factor(callout_year)2010 1.361e-01
## as.factor(callout_year)2011 1.362e-01
## as.factor(callout_dayofweek)monday 9.098e-02
## as.factor(callout_dayofweek)saturday 9.959e-02
## as.factor(callout_dayofweek)sunday 9.856e-02
## as.factor(callout_dayofweek)thursday 9.181e-02
## as.factor(callout_dayofweek)tuesday 9.138e-02
## as.factor(callout_dayofweek)wednesday 9.020e-02
## as.factor(callout_wardid == 1)TRUE 1.033e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 1.330e-01
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.803e-01
## MED_SERVICETRUE 9.621e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 2.797e-01
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 5.090e-02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 1.660e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 1.390e-01
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.879e-01
## z value
## (Intercept) 5.645
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.311
## cut2(oasis, g = 3)[27,34) -3.192
## cut2(oasis, g = 3)[34,64] -5.982
## cut2(age, g = 3)[56.1,73.8) -0.390
## cut2(age, g = 3)[73.8,91.4] 1.966
## female 0.720
## request_tele -0.442
## request_resp -1.820
## request_mrsa -1.449
## request_vre -5.570
## request_cdiff -3.293
## cut2(elixhauser_hospital, g = 3)[ 1, 7) -5.936
## cut2(elixhauser_hospital, g = 3)[ 7,31] -11.388
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) -2.460
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) -8.483
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) -15.949
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] -6.449
## as.factor(callout_month)2 -0.340
## as.factor(callout_month)3 0.633
## as.factor(callout_month)4 -1.622
## as.factor(callout_month)5 0.343
## as.factor(callout_month)6 -0.030
## as.factor(callout_month)7 0.210
## as.factor(callout_month)8 0.000
## as.factor(callout_month)9 -0.424
## as.factor(callout_month)10 0.477
## as.factor(callout_month)11 1.733
## as.factor(callout_month)12 0.625
## as.factor(callout_year)2006 0.189
## as.factor(callout_year)2007 1.209
## as.factor(callout_year)2008 2.413
## as.factor(callout_year)2009 2.913
## as.factor(callout_year)2010 1.825
## as.factor(callout_year)2011 3.060
## as.factor(callout_dayofweek)monday -0.885
## as.factor(callout_dayofweek)saturday 0.868
## as.factor(callout_dayofweek)sunday -0.067
## as.factor(callout_dayofweek)thursday -0.071
## as.factor(callout_dayofweek)tuesday -0.320
## as.factor(callout_dayofweek)wednesday -1.248
## as.factor(callout_wardid == 1)TRUE 5.028
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.363
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 1.022
## MED_SERVICETRUE 0.154
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) -1.832
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) -1.146
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] -0.685
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.226
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.150
## Pr(>|z|)
## (Intercept) 1.65e-08
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.75568
## cut2(oasis, g = 3)[27,34) 0.00141
## cut2(oasis, g = 3)[34,64] 2.20e-09
## cut2(age, g = 3)[56.1,73.8) 0.69620
## cut2(age, g = 3)[73.8,91.4] 0.04934
## female 0.47161
## request_tele 0.65872
## request_resp 0.06873
## request_mrsa 0.14733
## request_vre 2.55e-08
## request_cdiff 0.00099
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 2.91e-09
## cut2(elixhauser_hospital, g = 3)[ 7,31] < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.01391
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 1.13e-10
## as.factor(callout_month)2 0.73397
## as.factor(callout_month)3 0.52699
## as.factor(callout_month)4 0.10485
## as.factor(callout_month)5 0.73130
## as.factor(callout_month)6 0.97607
## as.factor(callout_month)7 0.83405
## as.factor(callout_month)8 0.99995
## as.factor(callout_month)9 0.67155
## as.factor(callout_month)10 0.63301
## as.factor(callout_month)11 0.08303
## as.factor(callout_month)12 0.53207
## as.factor(callout_year)2006 0.85019
## as.factor(callout_year)2007 0.22663
## as.factor(callout_year)2008 0.01580
## as.factor(callout_year)2009 0.00358
## as.factor(callout_year)2010 0.06806
## as.factor(callout_year)2011 0.00222
## as.factor(callout_dayofweek)monday 0.37630
## as.factor(callout_dayofweek)saturday 0.38520
## as.factor(callout_dayofweek)sunday 0.94666
## as.factor(callout_dayofweek)thursday 0.94309
## as.factor(callout_dayofweek)tuesday 0.74862
## as.factor(callout_dayofweek)wednesday 0.21216
## as.factor(callout_wardid == 1)TRUE 4.96e-07
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.71671
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.30697
## MED_SERVICETRUE 0.87765
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.06698
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.25166
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.49342
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.82156
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.88075
##
## (Intercept) ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE
## cut2(oasis, g = 3)[27,34) **
## cut2(oasis, g = 3)[34,64] ***
## cut2(age, g = 3)[56.1,73.8)
## cut2(age, g = 3)[73.8,91.4] *
## female
## request_tele
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3)[ 1, 7) ***
## cut2(elixhauser_hospital, g = 3)[ 7,31] ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) *
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] ***
## as.factor(callout_month)2
## as.factor(callout_month)3
## as.factor(callout_month)4
## as.factor(callout_month)5
## as.factor(callout_month)6
## as.factor(callout_month)7
## as.factor(callout_month)8
## as.factor(callout_month)9
## as.factor(callout_month)10
## as.factor(callout_month)11 .
## as.factor(callout_month)12
## as.factor(callout_year)2006
## as.factor(callout_year)2007
## as.factor(callout_year)2008 *
## as.factor(callout_year)2009 **
## as.factor(callout_year)2010 .
## as.factor(callout_year)2011 **
## as.factor(callout_dayofweek)monday
## as.factor(callout_dayofweek)saturday
## as.factor(callout_dayofweek)sunday
## as.factor(callout_dayofweek)thursday
## as.factor(callout_dayofweek)tuesday
## as.factor(callout_dayofweek)wednesday
## as.factor(callout_wardid == 1)TRUE ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## MED_SERVICETRUE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) .
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 11226 on 9672 degrees of freedom
## Residual deviance: 10335 on 9623 degrees of freedom
## AIC: 10435
##
## Number of Fisher Scoring iterations: 4## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10335
## cut2(oasis, g = 3) 2 10371
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_tele 1 10336
## request_resp 1 10338
## request_mrsa 1 10337
## request_vre 1 10365
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10683
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## MED_SERVICE 1 10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10335
## AIC
## <none> 10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10433
## cut2(oasis, g = 3) 10467
## cut2(age, g = 3) 10438
## female 10434
## request_tele 10434
## request_resp 10436
## request_mrsa 10435
## request_vre 10463
## request_cdiff 10444
## cut2(elixhauser_hospital, g = 3) 10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10775
## as.factor(callout_month) 10427
## as.factor(callout_year) 10452
## as.factor(callout_dayofweek) 10428
## MED_SERVICE 10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 10431## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10335
## cut2(oasis, g = 3) 2 10371
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_tele 1 10336
## request_resp 1 10338
## request_mrsa 1 10337
## request_vre 1 10365
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10467
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10683
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## MED_SERVICE 1 10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10335
## AIC
## <none> 10435
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10433
## cut2(oasis, g = 3) 10467
## cut2(age, g = 3) 10438
## female 10434
## request_tele 10434
## request_resp 10436
## request_mrsa 10435
## request_vre 10463
## request_cdiff 10444
## cut2(elixhauser_hospital, g = 3) 10563
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10775
## as.factor(callout_month) 10427
## as.factor(callout_year) 10452
## as.factor(callout_dayofweek) 10428
## MED_SERVICE 10433
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10434
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 10431
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.10
## cut2(oasis, g = 3) 35.86
## cut2(age, g = 3) 6.90
## female 0.52
## request_tele 0.19
## request_resp 3.17
## request_mrsa 2.08
## request_vre 30.09
## request_cdiff 10.58
## cut2(elixhauser_hospital, g = 3) 131.80
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 348.11
## as.factor(callout_month) 13.82
## as.factor(callout_year) 29.07
## as.factor(callout_dayofweek) 4.64
## MED_SERVICE 0.02
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.52
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.12
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.755910
## cut2(oasis, g = 3) 1.637e-08
## cut2(age, g = 3) 0.031693
## female 0.471577
## request_tele 0.658857
## request_resp 0.074821
## request_mrsa 0.149049
## request_vre 4.124e-08
## request_cdiff 0.001141
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.242854
## as.factor(callout_year) 5.909e-05
## as.factor(callout_dayofweek) 0.591136
## MED_SERVICE 0.877728
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.210127
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.942011
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_tele
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.942011## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10336
## cut2(oasis, g = 3) 2 10371
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_tele 1 10336
## request_resp 1 10338
## request_mrsa 1 10338
## request_vre 1 10366
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10684
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## as.factor(callout_wardid == 1) 1 10396
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10338
## MED_SERVICE 1 10335
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## AIC
## <none> 10431
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10430
## cut2(oasis, g = 3) 10463
## cut2(age, g = 3) 10434
## female 10430
## request_tele 10430
## request_resp 10432
## request_mrsa 10432
## request_vre 10460
## request_cdiff 10440
## cut2(elixhauser_hospital, g = 3) 10560
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10772
## as.factor(callout_month) 10423
## as.factor(callout_year) 10448
## as.factor(callout_dayofweek) 10424
## as.factor(callout_wardid == 1) 10490
## cut2(PROPFULL_BEDS, c(0.9, 1)) 10430
## MED_SERVICE 10429
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10430
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.10
## cut2(oasis, g = 3) 35.94
## cut2(age, g = 3) 6.92
## female 0.51
## request_tele 0.20
## request_resp 3.16
## request_mrsa 2.08
## request_vre 30.08
## request_cdiff 10.58
## cut2(elixhauser_hospital, g = 3) 132.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 348.08
## as.factor(callout_month) 13.86
## as.factor(callout_year) 29.00
## as.factor(callout_dayofweek) 4.63
## as.factor(callout_wardid == 1) 61.09
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2.86
## MED_SERVICE 0.03
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.50
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.749877
## cut2(oasis, g = 3) 1.567e-08
## cut2(age, g = 3) 0.031357
## female 0.473340
## request_tele 0.656905
## request_resp 0.075464
## request_mrsa 0.148811
## request_vre 4.138e-08
## request_cdiff 0.001146
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.241032
## as.factor(callout_year) 6.094e-05
## as.factor(callout_dayofweek) 0.592587
## as.factor(callout_wardid == 1) 5.440e-15
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.239400
## MED_SERVICE 0.868893
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.212105
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_tele
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "MED_SERVICE"## [1] 0.8688934## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10335
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10336
## cut2(oasis, g = 3) 2 10371
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_tele 1 10336
## request_resp 1 10339
## request_mrsa 1 10338
## request_vre 1 10366
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10685
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## as.factor(callout_wardid == 1) 1 10400
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10338
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## AIC
## <none> 10429
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10428
## cut2(oasis, g = 3) 10461
## cut2(age, g = 3) 10432
## female 10428
## request_tele 10428
## request_resp 10431
## request_mrsa 10430
## request_vre 10458
## request_cdiff 10438
## cut2(elixhauser_hospital, g = 3) 10558
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10771
## as.factor(callout_month) 10421
## as.factor(callout_year) 10446
## as.factor(callout_dayofweek) 10422
## as.factor(callout_wardid == 1) 10492
## cut2(PROPFULL_BEDS, c(0.9, 1)) 10428
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10428
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.10
## cut2(oasis, g = 3) 35.99
## cut2(age, g = 3) 6.90
## female 0.51
## request_tele 0.20
## request_resp 3.15
## request_mrsa 2.07
## request_vre 30.06
## request_cdiff 10.56
## cut2(elixhauser_hospital, g = 3) 132.40
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 349.56
## as.factor(callout_month) 13.84
## as.factor(callout_year) 28.97
## as.factor(callout_dayofweek) 4.61
## as.factor(callout_wardid == 1) 65.09
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2.86
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.50
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.751246
## cut2(oasis, g = 3) 1.527e-08
## cut2(age, g = 3) 0.031673
## female 0.474119
## request_tele 0.654468
## request_resp 0.076015
## request_mrsa 0.150224
## request_vre 4.194e-08
## request_cdiff 0.001154
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.241790
## as.factor(callout_year) 6.166e-05
## as.factor(callout_dayofweek) 0.594267
## as.factor(callout_wardid == 1) 7.167e-16
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.239751
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.212215
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_tele
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_tele"## [1] 0.6544675## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10336
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10336
## cut2(oasis, g = 3) 2 10372
## cut2(age, g = 3) 2 10342
## female 1 10336
## request_resp 1 10339
## request_mrsa 1 10338
## request_vre 1 10366
## request_cdiff 1 10346
## cut2(elixhauser_hospital, g = 3) 2 10468
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10685
## as.factor(callout_month) 11 10349
## as.factor(callout_year) 6 10364
## as.factor(callout_dayofweek) 6 10340
## as.factor(callout_wardid == 1) 1 10401
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10338
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10340
## AIC
## <none> 10428
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10426
## cut2(oasis, g = 3) 10460
## cut2(age, g = 3) 10430
## female 10426
## request_resp 10429
## request_mrsa 10428
## request_vre 10456
## request_cdiff 10436
## cut2(elixhauser_hospital, g = 3) 10556
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10769
## as.factor(callout_month) 10419
## as.factor(callout_year) 10444
## as.factor(callout_dayofweek) 10420
## as.factor(callout_wardid == 1) 10491
## cut2(PROPFULL_BEDS, c(0.9, 1)) 10426
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10426
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.10
## cut2(oasis, g = 3) 35.88
## cut2(age, g = 3) 6.78
## female 0.52
## request_resp 3.12
## request_mrsa 2.06
## request_vre 29.99
## request_cdiff 10.54
## cut2(elixhauser_hospital, g = 3) 132.86
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 349.59
## as.factor(callout_month) 13.80
## as.factor(callout_year) 28.78
## as.factor(callout_dayofweek) 4.58
## as.factor(callout_wardid == 1) 65.16
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2.85
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.54
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.746469
## cut2(oasis, g = 3) 1.616e-08
## cut2(age, g = 3) 0.033697
## female 0.470529
## request_resp 0.077450
## request_mrsa 0.151243
## request_vre 4.338e-08
## request_cdiff 0.001169
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.244042
## as.factor(callout_year) 6.702e-05
## as.factor(callout_dayofweek) 0.598040
## as.factor(callout_wardid == 1) 6.901e-16
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.239943
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.208739
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_dayofweek)"## [1] 0.5980398## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10340
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10340
## cut2(oasis, g = 3) 2 10376
## cut2(age, g = 3) 2 10347
## female 1 10341
## request_resp 1 10343
## request_mrsa 1 10342
## request_vre 1 10371
## request_cdiff 1 10350
## cut2(elixhauser_hospital, g = 3) 2 10473
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10692
## as.factor(callout_month) 11 10353
## as.factor(callout_year) 6 10369
## as.factor(callout_wardid == 1) 1 10406
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 10342
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10345
## AIC
## <none> 10420
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10418
## cut2(oasis, g = 3) 10452
## cut2(age, g = 3) 10423
## female 10419
## request_resp 10421
## request_mrsa 10420
## request_vre 10449
## request_cdiff 10428
## cut2(elixhauser_hospital, g = 3) 10549
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10764
## as.factor(callout_month) 10411
## as.factor(callout_year) 10437
## as.factor(callout_wardid == 1) 10484
## cut2(PROPFULL_BEDS, c(0.9, 1)) 10418
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10419
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.04
## cut2(oasis, g = 3) 36.19
## cut2(age, g = 3) 6.84
## female 0.55
## request_resp 3.09
## request_mrsa 2.08
## request_vre 30.50
## request_cdiff 10.29
## cut2(elixhauser_hospital, g = 3) 133.04
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 351.60
## as.factor(callout_month) 13.09
## as.factor(callout_year) 28.62
## as.factor(callout_wardid == 1) 66.27
## cut2(PROPFULL_BEDS, c(0.9, 1)) 1.37
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.80
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.849001
## cut2(oasis, g = 3) 1.387e-08
## cut2(age, g = 3) 0.032693
## female 0.457920
## request_resp 0.078601
## request_mrsa 0.149247
## request_vre 3.338e-08
## request_cdiff 0.001338
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.287674
## as.factor(callout_year) 7.166e-05
## as.factor(callout_wardid == 1) 3.937e-16
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.503443
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.186647
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.5034429## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10342
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10342
## cut2(oasis, g = 3) 2 10378
## cut2(age, g = 3) 2 10348
## female 1 10342
## request_resp 1 10345
## request_mrsa 1 10344
## request_vre 1 10372
## request_cdiff 1 10352
## cut2(elixhauser_hospital, g = 3) 2 10474
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10694
## as.factor(callout_month) 11 10354
## as.factor(callout_year) 6 10370
## as.factor(callout_wardid == 1) 1 10408
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10346
## AIC
## <none> 10418
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10416
## cut2(oasis, g = 3) 10450
## cut2(age, g = 3) 10420
## female 10416
## request_resp 10419
## request_mrsa 10418
## request_vre 10446
## request_cdiff 10426
## cut2(elixhauser_hospital, g = 3) 10546
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10762
## as.factor(callout_month) 10408
## as.factor(callout_year) 10434
## as.factor(callout_wardid == 1) 10482
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10416
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.00
## cut2(oasis, g = 3) 36.40
## cut2(age, g = 3) 6.78
## female 0.56
## request_resp 3.04
## request_mrsa 2.06
## request_vre 30.88
## request_cdiff 10.30
## cut2(elixhauser_hospital, g = 3) 132.47
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 352.08
## as.factor(callout_month) 12.86
## as.factor(callout_year) 28.60
## as.factor(callout_wardid == 1) 65.94
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.85
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.984937
## cut2(oasis, g = 3) 1.248e-08
## cut2(age, g = 3) 0.033681
## female 0.453775
## request_resp 0.080990
## request_mrsa 0.151565
## request_vre 2.746e-08
## request_cdiff 0.001333
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.302880
## as.factor(callout_year) 7.252e-05
## as.factor(callout_wardid == 1) 4.649e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.183393
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## female
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "female"## [1] 0.4537755## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10342
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10342
## cut2(oasis, g = 3) 2 10378
## cut2(age, g = 3) 2 10349
## request_resp 1 10345
## request_mrsa 1 10344
## request_vre 1 10373
## request_cdiff 1 10352
## cut2(elixhauser_hospital, g = 3) 2 10477
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10694
## as.factor(callout_month) 11 10355
## as.factor(callout_year) 6 10371
## as.factor(callout_wardid == 1) 1 10408
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10347
## AIC
## <none> 10416
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10414
## cut2(oasis, g = 3) 10448
## cut2(age, g = 3) 10419
## request_resp 10417
## request_mrsa 10416
## request_vre 10445
## request_cdiff 10424
## cut2(elixhauser_hospital, g = 3) 10547
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10760
## as.factor(callout_month) 10407
## as.factor(callout_year) 10433
## as.factor(callout_wardid == 1) 10480
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10415
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.00
## cut2(oasis, g = 3) 36.18
## cut2(age, g = 3) 7.05
## request_resp 3.07
## request_mrsa 2.10
## request_vre 30.67
## request_cdiff 10.24
## cut2(elixhauser_hospital, g = 3) 134.84
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 351.73
## as.factor(callout_month) 12.76
## as.factor(callout_year) 28.44
## as.factor(callout_wardid == 1) 65.86
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.89
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.968514
## cut2(oasis, g = 3) 1.391e-08
## cut2(age, g = 3) 0.029411
## request_resp 0.079574
## request_mrsa 0.147255
## request_vre 3.053e-08
## request_cdiff 0.001371
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.308979
## as.factor(callout_year) 7.764e-05
## as.factor(callout_wardid == 1) 4.837e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.180350
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## request_resp .
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_month)"## [1] 0.308979## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1) + relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 10355
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10355
## cut2(oasis, g = 3) 2 10391
## cut2(age, g = 3) 2 10362
## request_resp 1 10358
## request_mrsa 1 10357
## request_vre 1 10386
## request_cdiff 1 10366
## cut2(elixhauser_hospital, g = 3) 2 10489
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10704
## as.factor(callout_year) 6 10381
## as.factor(callout_wardid == 1) 1 10421
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 10360
## AIC
## <none> 10407
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10405
## cut2(oasis, g = 3) 10439
## cut2(age, g = 3) 10410
## request_resp 10408
## request_mrsa 10407
## request_vre 10436
## request_cdiff 10416
## cut2(elixhauser_hospital, g = 3) 10537
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10748
## as.factor(callout_year) 10421
## as.factor(callout_wardid == 1) 10471
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 10406
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.00
## cut2(oasis, g = 3) 35.84
## cut2(age, g = 3) 6.90
## request_resp 2.70
## request_mrsa 2.16
## request_vre 30.60
## request_cdiff 10.72
## cut2(elixhauser_hospital, g = 3) 133.81
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 348.85
## as.factor(callout_year) 25.90
## as.factor(callout_wardid == 1) 66.12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 4.71
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9538684
## cut2(oasis, g = 3) 1.646e-08
## cut2(age, g = 3) 0.0318001
## request_resp 0.1000410
## request_mrsa 0.1413777
## request_vre 3.165e-08
## request_cdiff 0.0010572
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.0002324
## as.factor(callout_wardid == 1) 4.242e-16
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.1942305
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) *
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"## [1] 0.1942305## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_resp + request_mrsa + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1)
## Df Deviance
## <none> 10360
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10360
## cut2(oasis, g = 3) 2 10396
## cut2(age, g = 3) 2 10367
## request_resp 1 10362
## request_mrsa 1 10362
## request_vre 1 10390
## request_cdiff 1 10370
## cut2(elixhauser_hospital, g = 3) 2 10494
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10710
## as.factor(callout_year) 6 10384
## as.factor(callout_wardid == 1) 1 10426
## AIC LRT
## <none> 10406
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404 0.01
## cut2(oasis, g = 3) 10438 36.04
## cut2(age, g = 3) 10409 7.02
## request_resp 10406 2.57
## request_mrsa 10406 2.16
## request_vre 10434 30.78
## request_cdiff 10414 10.60
## cut2(elixhauser_hospital, g = 3) 10536 134.19
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10748 350.35
## as.factor(callout_year) 10418 24.90
## as.factor(callout_wardid == 1) 10470 65.97
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9128998
## cut2(oasis, g = 3) 1.489e-08 ***
## cut2(age, g = 3) 0.0298308 *
## request_resp 0.1089144
## request_mrsa 0.1416814
## request_vre 2.884e-08 ***
## request_cdiff 0.0011290 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0003568 ***
## as.factor(callout_wardid == 1) 4.580e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_mrsa"## [1] 0.1416814## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_resp + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1)
## Df Deviance
## <none> 10362
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10362
## cut2(oasis, g = 3) 2 10398
## cut2(age, g = 3) 2 10369
## request_resp 1 10364
## request_vre 1 10396
## request_cdiff 1 10372
## cut2(elixhauser_hospital, g = 3) 2 10496
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10716
## as.factor(callout_year) 6 10386
## as.factor(callout_wardid == 1) 1 10426
## AIC LRT
## <none> 10406
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404 0.04
## cut2(oasis, g = 3) 10438 36.41
## cut2(age, g = 3) 10409 6.97
## request_resp 10406 2.66
## request_vre 10438 34.36
## request_cdiff 10414 10.62
## cut2(elixhauser_hospital, g = 3) 10536 134.14
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10752 353.93
## as.factor(callout_year) 10418 24.49
## as.factor(callout_wardid == 1) 10468 64.69
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8495831
## cut2(oasis, g = 3) 1.243e-08 ***
## cut2(age, g = 3) 0.0306513 *
## request_resp 0.1025802
## request_vre 4.573e-09 ***
## request_cdiff 0.0011206 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0004238 ***
## as.factor(callout_wardid == 1) 8.749e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_resp"## [1] 0.1025802## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS > 21) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1)
## Df Deviance
## <none> 10364
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 10364
## cut2(oasis, g = 3) 2 10401
## cut2(age, g = 3) 2 10372
## request_vre 1 10399
## request_cdiff 1 10375
## cut2(elixhauser_hospital, g = 3) 2 10499
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 10716
## as.factor(callout_year) 6 10388
## as.factor(callout_wardid == 1) 1 10429
## AIC LRT
## <none> 10406
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 10404 0.04
## cut2(oasis, g = 3) 10439 36.47
## cut2(age, g = 3) 10410 7.19
## request_vre 10439 34.23
## request_cdiff 10415 10.72
## cut2(elixhauser_hospital, g = 3) 10537 134.28
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 10750 351.96
## as.factor(callout_year) 10418 23.99
## as.factor(callout_wardid == 1) 10469 64.66
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8437574
## cut2(oasis, g = 3) 1.201e-08 ***
## cut2(age, g = 3) 0.0274167 *
## request_vre 4.908e-09 ***
## request_cdiff 0.0010587 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.0005246 ***
## as.factor(callout_wardid == 1) 8.898e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(age, g = 3)"## [1] 0.02741666
| I(HOSP_FREE_DAYS > 21) | ||||
| Odds Ratio | CI | p | ||
| (Intercept) | 3.69 | 2.76 – 4.96 | <.001 | |
| I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”) | 0.98 | 0.84 – 1.15 | .844 | |
| cut2(oasis, g = 3) | ||||
| [27,34) | 0.82 | 0.73 – 0.92 | .001 | |
| [34,64] | 0.68 | 0.59 – 0.77 | <.001 | |
| cut2(age, g = 3) | ||||
| [56.1,73.8) | 0.97 | 0.86 – 1.09 | .631 | |
| [73.8,91.4] | 1.14 | 1.00 – 1.29 | .050 | |
| request_vre | 0.55 | 0.45 – 0.67 | <.001 | |
| request_cdiff | 0.71 | 0.58 – 0.87 | <.001 | |
| cut2(elixhauser_hospital, g = 3) | ||||
| [ 1, 7) | 0.68 | 0.61 – 0.77 | <.001 | |
| [ 7,31] | 0.50 | 0.44 – 0.56 | <.001 | |
| cut2(los_pre_callout_days, c(1, 3, 7, 28)) | ||||
| [ 1.000, 3.000) | 0.84 | 0.74 – 0.96 | .008 | |
| [ 3.000, 7.000) | 0.53 | 0.46 – 0.61 | <.001 | |
| [ 7.000, 28.000) | 0.27 | 0.23 – 0.32 | <.001 | |
| [ 28.000,130.762] | 0.12 | 0.06 – 0.22 | <.001 | |
| as.factor(callout_year) | ||||
| 2006 | 1.01 | 0.77 – 1.32 | .938 | |
| 2007 | 1.16 | 0.89 – 1.50 | .262 | |
| 2008 | 1.37 | 1.05 – 1.77 | .018 | |
| 2009 | 1.40 | 1.07 – 1.81 | .012 | |
| 2010 | 1.26 | 0.97 – 1.63 | .077 | |
| 2011 | 1.44 | 1.11 – 1.86 | .006 | |
| as.factor(callout_wardid == 1) (TRUE) | 1.69 | 1.49 – 1.92 | <.001 | |
| Observations | 9673 | |||
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.984
## cut2(oasis, g = 3)[27,34) 0.822
## cut2(oasis, g = 3)[34,64] 0.675
## cut2(age, g = 3)[56.1,73.8) 0.971
## cut2(age, g = 3)[73.8,91.4] 1.136
## request_vre 0.549
## request_cdiff 0.711
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.685
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.496
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.843
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.532
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.270
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.120
## as.factor(callout_year)2006 1.011
## as.factor(callout_year)2007 1.161
## as.factor(callout_year)2008 1.369
## as.factor(callout_year)2009 1.396
## as.factor(callout_year)2010 1.261
## as.factor(callout_year)2011 1.442
## as.factor(callout_wardid == 1)TRUE 1.691
## 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.843
## cut2(oasis, g = 3)[27,34) 0.731
## cut2(oasis, g = 3)[34,64] 0.594
## cut2(age, g = 3)[56.1,73.8) 0.862
## cut2(age, g = 3)[73.8,91.4] 1.000
## request_vre 0.451
## request_cdiff 0.582
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.606
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.440
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.744
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.461
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.230
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.062
## as.factor(callout_year)2006 0.771
## as.factor(callout_year)2007 0.893
## as.factor(callout_year)2008 1.054
## as.factor(callout_year)2009 1.074
## as.factor(callout_year)2010 0.972
## as.factor(callout_year)2011 1.110
## as.factor(callout_wardid == 1)TRUE 1.490
## 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 1.152
## cut2(oasis, g = 3)[27,34) 0.925
## cut2(oasis, g = 3)[34,64] 0.767
## cut2(age, g = 3)[56.1,73.8) 1.094
## cut2(age, g = 3)[73.8,91.4] 1.290
## request_vre 0.670
## request_cdiff 0.871
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.774
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.559
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.956
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.613
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.316
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.221
## as.factor(callout_year)2006 1.319
## as.factor(callout_year)2007 1.503
## as.factor(callout_year)2008 1.771
## as.factor(callout_year)2009 1.807
## as.factor(callout_year)2010 1.627
## as.factor(callout_year)2011 1.864
## as.factor(callout_wardid == 1)TRUE 1.917
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.844
## cut2(oasis, g = 3)[27,34) 0.001
## cut2(oasis, g = 3)[34,64] 0.000
## cut2(age, g = 3)[56.1,73.8) 0.631
## cut2(age, g = 3)[73.8,91.4] 0.050
## request_vre 0.000
## request_cdiff 0.001
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.000
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.008
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.000
## as.factor(callout_year)2006 0.938
## as.factor(callout_year)2007 0.262
## as.factor(callout_year)2008 0.018
## as.factor(callout_year)2009 0.012
## as.factor(callout_year)2010 0.077
## as.factor(callout_year)2011 0.006
## as.factor(callout_wardid == 1)TRUE 0.000Answer 4: After adjusting for potential confounders, there is no statistically significant evidence that a long delay produces a better HFD outcome (>21 days).
Question 5: Do people who have long discharge delays (>24 hours) have a “bad outcome” defined as a long post discharge LOS (>3 week) or death?
##
## FALSE TRUE
## 8839 834##
## FALSE TRUE
## 8662 1011##
## FALSE TRUE
## FALSE 7934 905
## TRUE 728 106##
## FALSE TRUE
## FALSE 0.91595474 0.89515331
## TRUE 0.08404526 0.10484669##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: with(d, table(HOSP_FREE_DAYS <= 7, cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]"))
## X-squared = 4.7117, df = 1, p-value = 0.02996| variable_name | level | FALSE | TRUE | p | test |
|---|---|---|---|---|---|
| n | 8839 | 834 | |||
| micu (%) | 1 | 8839 (100.0) | 834 (100.0) | NA | |
| age (mean (sd)) | 63.51 (18.07) | 65.50 (16.43) | 0.002 | ||
| callout_month (%) | 1 | 701 ( 7.9) | 73 ( 8.8) | 0.749 | |
| 2 | 677 ( 7.7) | 61 ( 7.3) | |||
| 3 | 656 ( 7.4) | 77 ( 9.2) | |||
| 4 | 660 ( 7.5) | 66 ( 7.9) | |||
| 5 | 726 ( 8.2) | 68 ( 8.2) | |||
| 6 | 687 ( 7.8) | 58 ( 7.0) | |||
| 7 | 714 ( 8.1) | 65 ( 7.8) | |||
| 8 | 798 ( 9.0) | 79 ( 9.5) | |||
| 9 | 776 ( 8.8) | 75 ( 9.0) | |||
| 10 | 843 ( 9.5) | 71 ( 8.5) | |||
| 11 | 762 ( 8.6) | 61 ( 7.3) | |||
| 12 | 839 ( 9.5) | 80 ( 9.6) | |||
| female (%) | 0 | 4503 ( 50.9) | 453 ( 54.3) | 0.068 | |
| 1 | 4336 ( 49.1) | 381 ( 45.7) | |||
| request_tele (%) | 0 | 5741 ( 65.0) | 577 ( 69.2) | 0.016 | |
| 1 | 3098 ( 35.0) | 257 ( 30.8) | |||
| request_resp (%) | 0 | 8688 ( 98.3) | 826 ( 99.0) | 0.138 | |
| 1 | 151 ( 1.7) | 8 ( 1.0) | |||
| request_cdiff (%) | 0 | 8430 ( 95.4) | 760 ( 91.1) | <0.001 | |
| 1 | 409 ( 4.6) | 74 ( 8.9) | |||
| request_mrsa (%) | 0 | 7695 ( 87.1) | 710 ( 85.1) | 0.128 | |
| 1 | 1144 ( 12.9) | 124 ( 14.9) | |||
| request_vre (%) | 0 | 8428 ( 95.4) | 754 ( 90.4) | <0.001 | |
| 1 | 411 ( 4.6) | 80 ( 9.6) | |||
| oasis (mean (sd)) | 29.54 (7.13) | 32.97 (8.22) | <0.001 | ||
| elixhauser_hospital (mean (sd)) | 3.18 (7.18) | 7.40 (7.22) | <0.001 | ||
| ethnicity (%) | White | 6295 ( 71.2) | 615 ( 73.7) | 0.025 | |
| African American/Black | 1392 ( 15.7) | 102 ( 12.2) | |||
| Other | 1152 ( 13.0) | 117 ( 14.0) | |||
| MED_SERVICE (%) | FALSE | 631 ( 7.1) | 61 ( 7.3) | 0.906 | |
| TRUE | 8208 ( 92.9) | 773 ( 92.7) | |||
| HOSP_FREE_DAYS (mean (sd)) | 23.29 (4.04) | 0.62 (1.61) | <0.001 | ||
| callout_dayofweek (%) | friday | 1321 ( 14.9) | 121 ( 14.5) | 0.316 | |
| monday | 1176 ( 13.3) | 124 ( 14.9) | |||
| saturday | 1196 ( 13.5) | 96 ( 11.5) | |||
| sunday | 1161 ( 13.1) | 106 ( 12.7) | |||
| thursday | 1284 ( 14.5) | 128 ( 15.3) | |||
| tuesday | 1285 ( 14.5) | 137 ( 16.4) | |||
| wednesday | 1416 ( 16.0) | 122 ( 14.6) | |||
| CALLOUT_DURING_NIGHT (%) | FALSE | 8783 ( 99.4) | 824 ( 98.8) | 0.094 | |
| TRUE | 56 ( 0.6) | 10 ( 1.2) | |||
| CALLOUT_DURING_ROUNDS (%) | FALSE | 3456 ( 39.1) | 360 ( 43.2) | 0.024 | |
| TRUE | 5383 ( 60.9) | 474 ( 56.8) | |||
| DISCHARGEDELAY_HOURS (mean (sd)) | 10.30 (10.14) | 11.11 (11.60) | 0.029 | ||
| hourofcallout2 (median [IQR]) | 11.38 [10.10, 13.13] | 11.60 [10.28, 13.52] | 0.012 | nonnorm | |
| PROPFULL_BEDS (mean (sd)) | 0.91 (0.09) | 0.91 (0.09) | 0.762 | ||
| postcalldaycat2 (%) | 0 | 7153 ( 80.9) | 647 ( 77.6) | 0.022 | |
| [1,5] | 1686 ( 19.1) | 187 ( 22.4) | |||
| los_preicu_days (median [IQR]) | 0.00 [0.00, 0.11] | 0.00 [0.00, 1.71] | <0.001 | nonnorm | |
| los_post_callout_days (median [IQR]) | 4.06 [2.25, 6.34] | 16.00 [4.89, 28.19] | <0.001 | nonnorm | |
| los_post_icu_days (median [IQR]) | 3.65 [1.92, 6.00] | 15.75 [4.46, 27.90] | <0.001 | nonnorm | |
| los_pre_callout_days (median [IQR]) | 1.72 [0.91, 3.46] | 3.55 [1.57, 7.91] | <0.001 | nonnorm | |
| callout_year (%) | 2005 | 335 ( 3.8) | 41 ( 4.9) | 0.002 | |
| 2006 | 953 ( 10.8) | 117 ( 14.0) | |||
| 2007 | 1282 ( 14.5) | 135 ( 16.2) | |||
| 2008 | 1509 ( 17.1) | 129 ( 15.5) | |||
| 2009 | 1530 ( 17.3) | 138 ( 16.5) | |||
| 2010 | 1583 ( 17.9) | 156 ( 18.7) | |||
| 2011 | 1647 ( 18.6) | 118 ( 14.1) | |||
| hospitaldeath (mean (sd)) | 0.00 (0.00) | 0.64 (0.48) | <0.001 |
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5035.7
## cut2(oasis, g = 3) 2 5114.3
## cut2(age, g = 3) 2 5040.7
## female 1 5037.7
## request_tele 1 5043.2
## request_resp 1 5036.5
## request_mrsa 1 5035.7
## request_vre 1 5050.6
## request_cdiff 1 5044.4
## cut2(elixhauser_hospital, g = 3) 2 5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5198.9
## as.factor(callout_month) 11 5046.9
## as.factor(callout_year) 6 5058.4
## as.factor(callout_dayofweek) 6 5040.4
## MED_SERVICE 1 5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5036.9
## AIC
## <none> 5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5133.7
## cut2(oasis, g = 3) 5210.3
## cut2(age, g = 3) 5136.7
## female 5135.7
## request_tele 5141.2
## request_resp 5134.5
## request_mrsa 5133.7
## request_vre 5148.6
## request_cdiff 5142.4
## cut2(elixhauser_hospital, g = 3) 5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5290.9
## as.factor(callout_month) 5124.9
## as.factor(callout_year) 5146.4
## as.factor(callout_dayofweek) 5128.4
## MED_SERVICE 5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5132.9
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.031
## cut2(oasis, g = 3) 78.704
## cut2(age, g = 3) 5.083
## female 2.109
## request_tele 7.538
## request_resp 0.869
## request_mrsa 0.062
## request_vre 14.966
## request_cdiff 8.748
## cut2(elixhauser_hospital, g = 3) 114.761
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 163.314
## as.factor(callout_month) 11.314
## as.factor(callout_year) 22.785
## as.factor(callout_dayofweek) 4.771
## MED_SERVICE 1.462
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.021
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 1.277
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8607004
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.0787439
## female 0.1464668
## request_tele 0.0060405
## request_resp 0.3512004
## request_mrsa 0.8040967
## request_vre 0.0001094
## request_cdiff 0.0030987
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4173211
## as.factor(callout_year) 0.0008717
## as.factor(callout_dayofweek) 0.5734966
## MED_SERVICE 0.2266808
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0712288
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.5280732
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_tele +
## request_vre + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_wardid == 1)
## Df Deviance
## <none> 5099.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5101.1
## cut2(oasis, g = 3) 2 5178.0
## request_tele 1 5110.0
## request_vre 1 5115.8
## cut2(elixhauser_hospital, g = 3) 2 5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5275.2
## as.factor(callout_wardid == 1) 1 5145.5
## AIC LRT
## <none> 5125.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5125.1 1.569
## cut2(oasis, g = 3) 5200.0 78.442
## request_tele 5134.0 10.409
## request_vre 5139.8 16.218
## cut2(elixhauser_hospital, g = 3) 5243.5 121.963
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5293.2 175.693
## as.factor(callout_wardid == 1) 5169.5 45.983
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.210416
## cut2(oasis, g = 3) < 2.2e-16 ***
## request_tele 0.001254 **
## request_vre 5.644e-05 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_wardid == 1) 1.193e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5056.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5056.4
## cut2(oasis, g = 3) 2 5137.9
## cut2(age, g = 3) 2 5061.7
## female 1 5058.6
## request_tele 1 5063.8
## request_vre 1 5072.5
## request_cdiff 1 5065.4
## cut2(elixhauser_hospital, g = 3) 2 5173.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5219.4
## as.factor(callout_year) 6 5078.2
## as.factor(callout_wardid == 1) 1 5105.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5063.6
## AIC
## <none> 5108.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5106.4
## cut2(oasis, g = 3) 5185.9
## cut2(age, g = 3) 5109.7
## female 5108.6
## request_tele 5113.8
## request_vre 5122.5
## request_cdiff 5115.4
## cut2(elixhauser_hospital, g = 3) 5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5263.4
## as.factor(callout_year) 5118.2
## as.factor(callout_wardid == 1) 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.6
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.002
## cut2(oasis, g = 3) 81.467
## cut2(age, g = 3) 5.315
## female 2.190
## request_tele 7.363
## request_vre 16.138
## request_cdiff 8.986
## cut2(elixhauser_hospital, g = 3) 117.126
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 162.980
## as.factor(callout_year) 21.842
## as.factor(callout_wardid == 1) 48.977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.227
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.960419
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.070133
## female 0.138896
## request_tele 0.006656
## request_vre 5.89e-05
## request_cdiff 0.002721
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.001293
## as.factor(callout_wardid == 1) 2.59e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.065003
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## glm(formula = I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS,
## c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7,
## 12, 19)), "[ 7.000,12.000)"), family = "binomial", data = d)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8597 -0.4415 -0.3211 -0.2346 2.9127
##
## Coefficients:
## Estimate
## (Intercept) -2.796181
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.021616
## cut2(oasis, g = 3)[27,34) 0.321378
## cut2(oasis, g = 3)[34,64] 0.881226
## cut2(age, g = 3)[56.1,73.8) -0.074754
## cut2(age, g = 3)[73.8,91.4] -0.226033
## female -0.111347
## request_tele -0.224724
## request_resp -0.334228
## request_mrsa 0.027336
## request_vre 0.567030
## request_cdiff 0.431446
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.520595
## cut2(elixhauser_hospital, g = 3)[ 7,31] 1.050986
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.002218
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.591122
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 1.076589
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 2.371992
## as.factor(callout_month)2 -0.151334
## as.factor(callout_month)3 0.036299
## as.factor(callout_month)4 -0.070657
## as.factor(callout_month)5 -0.164206
## as.factor(callout_month)6 -0.234734
## as.factor(callout_month)7 -0.179483
## as.factor(callout_month)8 -0.070305
## as.factor(callout_month)9 -0.184683
## as.factor(callout_month)10 -0.313408
## as.factor(callout_month)11 -0.465702
## as.factor(callout_month)12 -0.163809
## as.factor(callout_year)2006 -0.116078
## as.factor(callout_year)2007 -0.293361
## as.factor(callout_year)2008 -0.447500
## as.factor(callout_year)2009 -0.524895
## as.factor(callout_year)2010 -0.378814
## as.factor(callout_year)2011 -0.697084
## as.factor(callout_dayofweek)monday 0.128210
## as.factor(callout_dayofweek)saturday -0.019462
## as.factor(callout_dayofweek)sunday 0.029041
## as.factor(callout_dayofweek)thursday 0.055857
## as.factor(callout_dayofweek)tuesday 0.189420
## as.factor(callout_dayofweek)wednesday -0.069679
## as.factor(callout_wardid == 1)TRUE -0.573508
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.168689
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.052471
## MED_SERVICETRUE 0.185641
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.849213
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.096323
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.357861
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.209857
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.219414
## Std. Error
## (Intercept) 0.341984
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.123002
## cut2(oasis, g = 3)[27,34) 0.103669
## cut2(oasis, g = 3)[34,64] 0.104732
## cut2(age, g = 3)[56.1,73.8) 0.097007
## cut2(age, g = 3)[73.8,91.4] 0.103509
## female 0.076750
## request_tele 0.082644
## request_resp 0.374775
## request_mrsa 0.109974
## request_vre 0.140655
## request_cdiff 0.140900
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.112033
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.104206
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.110923
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.116433
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.121483
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.303472
## as.factor(callout_month)2 0.190685
## as.factor(callout_month)3 0.182247
## as.factor(callout_month)4 0.187913
## as.factor(callout_month)5 0.185860
## as.factor(callout_month)6 0.193114
## as.factor(callout_month)7 0.187621
## as.factor(callout_month)8 0.180723
## as.factor(callout_month)9 0.182913
## as.factor(callout_month)10 0.185124
## as.factor(callout_month)11 0.192103
## as.factor(callout_month)12 0.181808
## as.factor(callout_year)2006 0.207444
## as.factor(callout_year)2007 0.205283
## as.factor(callout_year)2008 0.206146
## as.factor(callout_year)2009 0.207046
## as.factor(callout_year)2010 0.204035
## as.factor(callout_year)2011 0.207898
## as.factor(callout_dayofweek)monday 0.141457
## as.factor(callout_dayofweek)saturday 0.159221
## as.factor(callout_dayofweek)sunday 0.155919
## as.factor(callout_dayofweek)thursday 0.143115
## as.factor(callout_dayofweek)tuesday 0.140669
## as.factor(callout_dayofweek)wednesday 0.145760
## as.factor(callout_wardid == 1)TRUE 0.153086
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.188279
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.254355
## MED_SERVICETRUE 0.156027
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.377964
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.079690
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.243742
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.200020
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.270608
## z value
## (Intercept) -8.176
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.176
## cut2(oasis, g = 3)[27,34) 3.100
## cut2(oasis, g = 3)[34,64] 8.414
## cut2(age, g = 3)[56.1,73.8) -0.771
## cut2(age, g = 3)[73.8,91.4] -2.184
## female -1.451
## request_tele -2.719
## request_resp -0.892
## request_mrsa 0.249
## request_vre 4.031
## request_cdiff 3.062
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 4.647
## cut2(elixhauser_hospital, g = 3)[ 7,31] 10.086
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.020
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 5.077
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 8.862
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 7.816
## as.factor(callout_month)2 -0.794
## as.factor(callout_month)3 0.199
## as.factor(callout_month)4 -0.376
## as.factor(callout_month)5 -0.883
## as.factor(callout_month)6 -1.216
## as.factor(callout_month)7 -0.957
## as.factor(callout_month)8 -0.389
## as.factor(callout_month)9 -1.010
## as.factor(callout_month)10 -1.693
## as.factor(callout_month)11 -2.424
## as.factor(callout_month)12 -0.901
## as.factor(callout_year)2006 -0.560
## as.factor(callout_year)2007 -1.429
## as.factor(callout_year)2008 -2.171
## as.factor(callout_year)2009 -2.535
## as.factor(callout_year)2010 -1.857
## as.factor(callout_year)2011 -3.353
## as.factor(callout_dayofweek)monday 0.906
## as.factor(callout_dayofweek)saturday -0.122
## as.factor(callout_dayofweek)sunday 0.186
## as.factor(callout_dayofweek)thursday 0.390
## as.factor(callout_dayofweek)tuesday 1.347
## as.factor(callout_dayofweek)wednesday -0.478
## as.factor(callout_wardid == 1)TRUE -3.746
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.896
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.206
## MED_SERVICETRUE 1.190
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 2.247
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 1.209
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 1.468
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -1.049
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.811
## Pr(>|z|)
## (Intercept) 2.93e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.860498
## cut2(oasis, g = 3)[27,34) 0.001935
## cut2(oasis, g = 3)[34,64] < 2e-16
## cut2(age, g = 3)[56.1,73.8) 0.440939
## cut2(age, g = 3)[73.8,91.4] 0.028984
## female 0.146843
## request_tele 0.006544
## request_resp 0.372495
## request_mrsa 0.803695
## request_vre 5.55e-05
## request_cdiff 0.002198
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 3.37e-06
## cut2(elixhauser_hospital, g = 3)[ 7,31] < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.984050
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 3.84e-07
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 5.45e-15
## as.factor(callout_month)2 0.427407
## as.factor(callout_month)3 0.842127
## as.factor(callout_month)4 0.706908
## as.factor(callout_month)5 0.376968
## as.factor(callout_month)6 0.224168
## as.factor(callout_month)7 0.338756
## as.factor(callout_month)8 0.697260
## as.factor(callout_month)9 0.312649
## as.factor(callout_month)10 0.090463
## as.factor(callout_month)11 0.015341
## as.factor(callout_month)12 0.367588
## as.factor(callout_year)2006 0.575776
## as.factor(callout_year)2007 0.152988
## as.factor(callout_year)2008 0.029947
## as.factor(callout_year)2009 0.011240
## as.factor(callout_year)2010 0.063366
## as.factor(callout_year)2011 0.000799
## as.factor(callout_dayofweek)monday 0.364750
## as.factor(callout_dayofweek)saturday 0.902717
## as.factor(callout_dayofweek)sunday 0.852242
## as.factor(callout_dayofweek)thursday 0.696321
## as.factor(callout_dayofweek)tuesday 0.178120
## as.factor(callout_dayofweek)wednesday 0.632620
## as.factor(callout_wardid == 1)TRUE 0.000179
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.370278
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.836565
## MED_SERVICETRUE 0.234126
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) 0.024652
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000) 0.226774
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867] 0.142051
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.294094
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.417471
##
## (Intercept) ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE
## cut2(oasis, g = 3)[27,34) **
## cut2(oasis, g = 3)[34,64] ***
## cut2(age, g = 3)[56.1,73.8)
## cut2(age, g = 3)[73.8,91.4] *
## female
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3)[ 1, 7) ***
## cut2(elixhauser_hospital, g = 3)[ 7,31] ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000)
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] ***
## as.factor(callout_month)2
## as.factor(callout_month)3
## as.factor(callout_month)4
## as.factor(callout_month)5
## as.factor(callout_month)6
## as.factor(callout_month)7
## as.factor(callout_month)8
## as.factor(callout_month)9
## as.factor(callout_month)10 .
## as.factor(callout_month)11 *
## as.factor(callout_month)12
## as.factor(callout_year)2006
## as.factor(callout_year)2007
## as.factor(callout_year)2008 *
## as.factor(callout_year)2009 *
## as.factor(callout_year)2010 .
## as.factor(callout_year)2011 ***
## as.factor(callout_dayofweek)monday
## as.factor(callout_dayofweek)saturday
## as.factor(callout_dayofweek)sunday
## as.factor(callout_dayofweek)thursday
## as.factor(callout_dayofweek)tuesday
## as.factor(callout_dayofweek)wednesday
## as.factor(callout_wardid == 1)TRUE ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## MED_SERVICETRUE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[ 0.117, 7.000) *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[12.000,19.000)
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)")[19.000,23.867]
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5682.0 on 9672 degrees of freedom
## Residual deviance: 5035.6 on 9623 degrees of freedom
## AIC: 5135.6
##
## Number of Fisher Scoring iterations: 6## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5035.7
## cut2(oasis, g = 3) 2 5114.3
## cut2(age, g = 3) 2 5040.7
## female 1 5037.7
## request_tele 1 5043.2
## request_resp 1 5036.5
## request_mrsa 1 5035.7
## request_vre 1 5050.6
## request_cdiff 1 5044.4
## cut2(elixhauser_hospital, g = 3) 2 5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5198.9
## as.factor(callout_month) 11 5046.9
## as.factor(callout_year) 6 5058.4
## as.factor(callout_dayofweek) 6 5040.4
## MED_SERVICE 1 5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5036.9
## AIC
## <none> 5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5133.7
## cut2(oasis, g = 3) 5210.3
## cut2(age, g = 3) 5136.7
## female 5135.7
## request_tele 5141.2
## request_resp 5134.5
## request_mrsa 5133.7
## request_vre 5148.6
## request_cdiff 5142.4
## cut2(elixhauser_hospital, g = 3) 5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5290.9
## as.factor(callout_month) 5124.9
## as.factor(callout_year) 5146.4
## as.factor(callout_dayofweek) 5128.4
## MED_SERVICE 5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5132.9## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5035.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5035.7
## cut2(oasis, g = 3) 2 5114.3
## cut2(age, g = 3) 2 5040.7
## female 1 5037.7
## request_tele 1 5043.2
## request_resp 1 5036.5
## request_mrsa 1 5035.7
## request_vre 1 5050.6
## request_cdiff 1 5044.4
## cut2(elixhauser_hospital, g = 3) 2 5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5198.9
## as.factor(callout_month) 11 5046.9
## as.factor(callout_year) 6 5058.4
## as.factor(callout_dayofweek) 6 5040.4
## MED_SERVICE 1 5037.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5036.9
## AIC
## <none> 5135.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5133.7
## cut2(oasis, g = 3) 5210.3
## cut2(age, g = 3) 5136.7
## female 5135.7
## request_tele 5141.2
## request_resp 5134.5
## request_mrsa 5133.7
## request_vre 5148.6
## request_cdiff 5142.4
## cut2(elixhauser_hospital, g = 3) 5246.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5290.9
## as.factor(callout_month) 5124.9
## as.factor(callout_year) 5146.4
## as.factor(callout_dayofweek) 5128.4
## MED_SERVICE 5135.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5136.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5132.9
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.031
## cut2(oasis, g = 3) 78.704
## cut2(age, g = 3) 5.083
## female 2.109
## request_tele 7.538
## request_resp 0.869
## request_mrsa 0.062
## request_vre 14.966
## request_cdiff 8.748
## cut2(elixhauser_hospital, g = 3) 114.761
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 163.314
## as.factor(callout_month) 11.314
## as.factor(callout_year) 22.785
## as.factor(callout_dayofweek) 4.771
## MED_SERVICE 1.462
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.021
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 1.277
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8607004
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.0787439
## female 0.1464668
## request_tele 0.0060405
## request_resp 0.3512004
## request_mrsa 0.8040967
## request_vre 0.0001094
## request_cdiff 0.0030987
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4173211
## as.factor(callout_year) 0.0008717
## as.factor(callout_dayofweek) 0.5734966
## MED_SERVICE 0.2266808
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0712288
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.5280732
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_mrsa
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_mrsa"## [1] 0.8040967## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)") + as.factor(callout_wardid ==
## 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## Df Deviance
## <none> 5035.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5035.7
## cut2(oasis, g = 3) 2 5114.5
## cut2(age, g = 3) 2 5040.7
## female 1 5037.8
## request_tele 1 5043.2
## request_resp 1 5036.6
## request_vre 1 5051.4
## request_cdiff 1 5044.4
## cut2(elixhauser_hospital, g = 3) 2 5150.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5199.5
## as.factor(callout_month) 11 5047.0
## as.factor(callout_year) 6 5058.4
## as.factor(callout_dayofweek) 6 5040.5
## MED_SERVICE 1 5037.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5042.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5037.0
## AIC
## <none> 5133.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5131.7
## cut2(oasis, g = 3) 5208.5
## cut2(age, g = 3) 5134.7
## female 5133.8
## request_tele 5139.2
## request_resp 5132.6
## request_vre 5147.4
## request_cdiff 5140.4
## cut2(elixhauser_hospital, g = 3) 5244.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5289.5
## as.factor(callout_month) 5123.0
## as.factor(callout_year) 5144.4
## as.factor(callout_dayofweek) 5126.5
## MED_SERVICE 5133.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5134.7
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5131.0
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.035
## cut2(oasis, g = 3) 78.759
## cut2(age, g = 3) 5.053
## female 2.114
## request_tele 7.537
## request_resp 0.860
## request_vre 15.690
## request_cdiff 8.730
## cut2(elixhauser_hospital, g = 3) 114.712
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 163.808
## as.factor(callout_month) 11.296
## as.factor(callout_year) 22.739
## as.factor(callout_dayofweek) 4.778
## MED_SERVICE 1.487
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.009
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 1.280
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8507003
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.0799442
## female 0.1459262
## request_tele 0.0060441
## request_resp 0.3537129
## request_vre 7.462e-05
## request_cdiff 0.0031299
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4188126
## as.factor(callout_year) 0.0008889
## as.factor(callout_dayofweek) 0.5725854
## MED_SERVICE 0.2226188
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0716202
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.5273840
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_dayofweek)
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_dayofweek)"## [1] 0.5725854## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)") + as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS,
## c(0.9, 1))
## Df Deviance
## <none> 5040.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5040.5
## cut2(oasis, g = 3) 2 5119.8
## cut2(age, g = 3) 2 5045.6
## female 1 5042.6
## request_tele 1 5048.0
## request_resp 1 5041.3
## request_vre 1 5056.4
## request_cdiff 1 5049.2
## cut2(elixhauser_hospital, g = 3) 2 5155.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5205.3
## as.factor(callout_month) 11 5051.5
## as.factor(callout_year) 6 5063.4
## MED_SERVICE 1 5041.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5047.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5041.7
## AIC
## <none> 5126.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5124.5
## cut2(oasis, g = 3) 5201.8
## cut2(age, g = 3) 5127.6
## female 5126.6
## request_tele 5132.0
## request_resp 5125.3
## request_vre 5140.4
## request_cdiff 5133.2
## cut2(elixhauser_hospital, g = 3) 5237.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5283.3
## as.factor(callout_month) 5115.5
## as.factor(callout_year) 5137.4
## MED_SERVICE 5125.9
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5127.9
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 5123.7
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.019
## cut2(oasis, g = 3) 79.284
## cut2(age, g = 3) 5.082
## female 2.137
## request_tele 7.541
## request_resp 0.867
## request_vre 15.886
## request_cdiff 8.726
## cut2(elixhauser_hospital, g = 3) 115.133
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 164.822
## as.factor(callout_month) 11.005
## as.factor(callout_year) 22.888
## MED_SERVICE 1.438
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.421
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 1.207
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.8903575
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.0787931
## female 0.1437780
## request_tele 0.0060302
## request_resp 0.3517111
## request_vre 6.728e-05
## request_cdiff 0.0031375
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4428466
## as.factor(callout_year) 0.0008348
## MED_SERVICE 0.2304560
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0596222
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.5467630
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.546763## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5041.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5041.7
## cut2(oasis, g = 3) 2 5120.7
## cut2(age, g = 3) 2 5046.8
## female 1 5043.7
## request_tele 1 5049.2
## request_resp 1 5042.6
## request_vre 1 5057.6
## request_cdiff 1 5050.7
## cut2(elixhauser_hospital, g = 3) 2 5157.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5205.9
## as.factor(callout_month) 11 5052.7
## as.factor(callout_year) 6 5064.7
## as.factor(callout_wardid == 1) 1 5091.6
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5042.8
## MED_SERVICE 1 5043.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5048.9
## AIC
## <none> 5123.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5121.7
## cut2(oasis, g = 3) 5198.7
## cut2(age, g = 3) 5124.8
## female 5123.7
## request_tele 5129.2
## request_resp 5122.6
## request_vre 5137.6
## request_cdiff 5130.7
## cut2(elixhauser_hospital, g = 3) 5235.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5279.9
## as.factor(callout_month) 5112.7
## as.factor(callout_year) 5134.7
## as.factor(callout_wardid == 1) 5171.6
## cut2(PROPFULL_BEDS, c(0.9, 1)) 5120.8
## MED_SERVICE 5123.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5124.9
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.012
## cut2(oasis, g = 3) 79.039
## cut2(age, g = 3) 5.146
## female 2.063
## request_tele 7.558
## request_resp 0.914
## request_vre 15.955
## request_cdiff 8.977
## cut2(elixhauser_hospital, g = 3) 116.095
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 164.171
## as.factor(callout_month) 11.011
## as.factor(callout_year) 22.987
## as.factor(callout_wardid == 1) 49.925
## cut2(PROPFULL_BEDS, c(0.9, 1)) 1.153
## MED_SERVICE 1.378
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.197
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.913085
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.076309
## female 0.150954
## request_tele 0.005973
## request_resp 0.339069
## request_vre 6.486e-05
## request_cdiff 0.002735
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.442316
## as.factor(callout_year) 0.000801
## as.factor(callout_wardid == 1) 1.598e-12
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.561851
## MED_SERVICE 0.240476
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.065883
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## cut2(PROPFULL_BEDS, c(0.9, 1))
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.5618508## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5042.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5042.8
## cut2(oasis, g = 3) 2 5122.2
## cut2(age, g = 3) 2 5048.1
## female 1 5044.9
## request_tele 1 5050.2
## request_resp 1 5043.8
## request_vre 1 5059.1
## request_cdiff 1 5051.6
## cut2(elixhauser_hospital, g = 3) 2 5158.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5206.9
## as.factor(callout_month) 11 5053.9
## as.factor(callout_year) 6 5066.1
## as.factor(callout_wardid == 1) 1 5093.0
## MED_SERVICE 1 5044.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5050.1
## AIC
## <none> 5120.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5118.8
## cut2(oasis, g = 3) 5196.2
## cut2(age, g = 3) 5122.1
## female 5120.9
## request_tele 5126.2
## request_resp 5119.8
## request_vre 5135.1
## request_cdiff 5127.6
## cut2(elixhauser_hospital, g = 3) 5232.6
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5276.9
## as.factor(callout_month) 5109.9
## as.factor(callout_year) 5132.1
## as.factor(callout_wardid == 1) 5169.0
## MED_SERVICE 5120.2
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5122.1
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 79.380
## cut2(age, g = 3) 5.243
## female 2.063
## request_tele 7.399
## request_resp 0.921
## request_vre 16.223
## request_cdiff 8.808
## cut2(elixhauser_hospital, g = 3) 115.796
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 164.025
## as.factor(callout_month) 11.077
## as.factor(callout_year) 23.306
## as.factor(callout_wardid == 1) 50.177
## MED_SERVICE 1.333
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.231
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.9884273
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.0726970
## female 0.1509506
## request_tele 0.0065267
## request_resp 0.3371746
## request_vre 5.629e-05
## request_cdiff 0.0029985
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4368298
## as.factor(callout_year) 0.0007003
## as.factor(callout_wardid == 1) 1.405e-12
## MED_SERVICE 0.2483479
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.0648942
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year) ***
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_month)"## [1] 0.4368298## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_vre + request_cdiff +
## cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_wardid ==
## 1) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5053.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5053.9
## cut2(oasis, g = 3) 2 5132.9
## cut2(age, g = 3) 2 5059.0
## female 1 5056.1
## request_tele 1 5061.3
## request_resp 1 5054.9
## request_vre 1 5069.8
## request_cdiff 1 5062.9
## cut2(elixhauser_hospital, g = 3) 2 5169.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5217.4
## as.factor(callout_year) 6 5075.2
## as.factor(callout_wardid == 1) 1 5104.3
## MED_SERVICE 1 5055.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5061.0
## AIC
## <none> 5109.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5107.9
## cut2(oasis, g = 3) 5184.9
## cut2(age, g = 3) 5111.0
## female 5110.1
## request_tele 5115.3
## request_resp 5108.9
## request_vre 5123.8
## request_cdiff 5116.9
## cut2(elixhauser_hospital, g = 3) 5221.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5265.4
## as.factor(callout_year) 5119.2
## as.factor(callout_wardid == 1) 5158.3
## MED_SERVICE 5109.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5111.0
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.005
## cut2(oasis, g = 3) 79.031
## cut2(age, g = 3) 5.087
## female 2.193
## request_tele 7.355
## request_resp 0.967
## request_vre 15.860
## request_cdiff 8.990
## cut2(elixhauser_hospital, g = 3) 115.459
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 163.536
## as.factor(callout_year) 21.262
## as.factor(callout_wardid == 1) 50.437
## MED_SERVICE 1.557
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.066
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.942685
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.078597
## female 0.138635
## request_tele 0.006689
## request_resp 0.325353
## request_vre 6.821e-05
## request_cdiff 0.002715
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.001646
## as.factor(callout_wardid == 1) 1.230e-12
## MED_SERVICE 0.212061
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.069828
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_resp
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_resp"## [1] 0.3253533## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5054.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5054.9
## cut2(oasis, g = 3) 2 5134.0
## cut2(age, g = 3) 2 5059.9
## female 1 5057.0
## request_tele 1 5062.2
## request_vre 1 5070.8
## request_cdiff 1 5063.8
## cut2(elixhauser_hospital, g = 3) 2 5170.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5219.3
## as.factor(callout_year) 6 5076.5
## as.factor(callout_wardid == 1) 1 5105.3
## MED_SERVICE 1 5056.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5062.1
## AIC
## <none> 5108.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5106.9
## cut2(oasis, g = 3) 5184.0
## cut2(age, g = 3) 5109.9
## female 5109.0
## request_tele 5114.2
## request_vre 5122.8
## request_cdiff 5115.8
## cut2(elixhauser_hospital, g = 3) 5220.3
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5265.3
## as.factor(callout_year) 5118.5
## as.factor(callout_wardid == 1) 5157.3
## MED_SERVICE 5108.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5110.1
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.004
## cut2(oasis, g = 3) 79.078
## cut2(age, g = 3) 4.997
## female 2.146
## request_tele 7.288
## request_vre 15.914
## request_cdiff 8.961
## cut2(elixhauser_hospital, g = 3) 115.392
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 164.461
## as.factor(callout_year) 21.600
## as.factor(callout_wardid == 1) 50.418
## MED_SERVICE 1.526
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.185
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.949394
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.082197
## female 0.142917
## request_tele 0.006943
## request_vre 6.629e-05
## request_cdiff 0.002758
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.001430
## as.factor(callout_wardid == 1) 1.242e-12
## MED_SERVICE 0.216783
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.066235
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## MED_SERVICE
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "MED_SERVICE"## [1] 0.2167831## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5056.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5056.4
## cut2(oasis, g = 3) 2 5137.9
## cut2(age, g = 3) 2 5061.7
## female 1 5058.6
## request_tele 1 5063.8
## request_vre 1 5072.5
## request_cdiff 1 5065.4
## cut2(elixhauser_hospital, g = 3) 2 5173.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5219.4
## as.factor(callout_year) 6 5078.2
## as.factor(callout_wardid == 1) 1 5105.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5063.6
## AIC
## <none> 5108.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5106.4
## cut2(oasis, g = 3) 5185.9
## cut2(age, g = 3) 5109.7
## female 5108.6
## request_tele 5113.8
## request_vre 5122.5
## request_cdiff 5115.4
## cut2(elixhauser_hospital, g = 3) 5221.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5263.4
## as.factor(callout_year) 5118.2
## as.factor(callout_wardid == 1) 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.6
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.002
## cut2(oasis, g = 3) 81.467
## cut2(age, g = 3) 5.315
## female 2.190
## request_tele 7.363
## request_vre 16.138
## request_cdiff 8.986
## cut2(elixhauser_hospital, g = 3) 117.126
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 162.980
## as.factor(callout_year) 21.842
## as.factor(callout_wardid == 1) 48.977
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.227
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.960419
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.070133
## female 0.138896
## request_tele 0.006656
## request_vre 5.89e-05
## request_cdiff 0.002721
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.001293
## as.factor(callout_wardid == 1) 2.59e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.065003
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## female
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "female"## [1] 0.1388955## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1) + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.000,12.000)")
## Df Deviance
## <none> 5058.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5058.6
## cut2(oasis, g = 3) 2 5139.4
## cut2(age, g = 3) 2 5064.3
## request_tele 1 5066.0
## request_vre 1 5074.4
## request_cdiff 1 5067.5
## cut2(elixhauser_hospital, g = 3) 2 5179.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5221.1
## as.factor(callout_year) 6 5080.1
## as.factor(callout_wardid == 1) 1 5107.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 3 5065.9
## AIC
## <none> 5108.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5106.6
## cut2(oasis, g = 3) 5185.4
## cut2(age, g = 3) 5110.3
## request_tele 5114.0
## request_vre 5122.4
## request_cdiff 5115.5
## cut2(elixhauser_hospital, g = 3) 5225.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5263.1
## as.factor(callout_year) 5118.1
## as.factor(callout_wardid == 1) 5155.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 5109.9
## LRT
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.000
## cut2(oasis, g = 3) 80.830
## cut2(age, g = 3) 5.657
## request_tele 7.408
## request_vre 15.772
## request_cdiff 8.880
## cut2(elixhauser_hospital, g = 3) 120.475
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 162.464
## as.factor(callout_year) 21.480
## as.factor(callout_wardid == 1) 48.770
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 7.309
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.997572
## cut2(oasis, g = 3) < 2.2e-16
## cut2(age, g = 3) 0.059102
## request_tele 0.006495
## request_vre 7.145e-05
## request_cdiff 0.002883
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_year) 0.001504
## as.factor(callout_wardid == 1) 2.878e-12
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") 0.062683
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) .
## request_tele **
## request_vre ***
## request_cdiff **
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_year) **
## as.factor(callout_wardid == 1) ***
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.000,12.000)") .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.000,12.000)\")"## [1] 0.06268348## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## request_tele + request_vre + request_cdiff + cut2(elixhauser_hospital,
## g = 3) + cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1)
## Df Deviance
## <none> 5065.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5065.9
## cut2(oasis, g = 3) 2 5147.7
## cut2(age, g = 3) 2 5071.8
## request_tele 1 5073.1
## request_vre 1 5082.0
## request_cdiff 1 5074.6
## cut2(elixhauser_hospital, g = 3) 2 5186.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5229.0
## as.factor(callout_year) 6 5086.2
## as.factor(callout_wardid == 1) 1 5114.3
## AIC LRT
## <none> 5109.9
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5107.9 0.005
## cut2(oasis, g = 3) 5187.7 81.785
## cut2(age, g = 3) 5111.8 5.870
## request_tele 5115.1 7.174
## request_vre 5124.0 16.053
## request_cdiff 5116.6 8.662
## cut2(elixhauser_hospital, g = 3) 5226.4 120.525
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5265.0 163.079
## as.factor(callout_year) 5118.2 20.332
## as.factor(callout_wardid == 1) 5156.3 48.382
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.944042
## cut2(oasis, g = 3) < 2.2e-16 ***
## cut2(age, g = 3) 0.053129 .
## request_tele 0.007397 **
## request_vre 6.161e-05 ***
## request_cdiff 0.003250 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.002416 **
## as.factor(callout_wardid == 1) 3.508e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "cut2(age, g = 3)"## [1] 0.05312853## Single term deletions
##
## Model:
## I(HOSP_FREE_DAYS <= 7) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + request_tele +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_year) +
## as.factor(callout_wardid == 1)
## Df Deviance
## <none> 5071.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 5071.8
## cut2(oasis, g = 3) 2 5147.8
## request_tele 1 5080.2
## request_vre 1 5088.5
## request_cdiff 1 5079.8
## cut2(elixhauser_hospital, g = 3) 2 5187.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 5242.1
## as.factor(callout_year) 6 5092.3
## as.factor(callout_wardid == 1) 1 5124.0
## AIC LRT
## <none> 5111.8
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 5109.8 0.015
## cut2(oasis, g = 3) 5183.8 76.061
## request_tele 5118.2 8.408
## request_vre 5126.5 16.682
## request_cdiff 5117.8 8.014
## cut2(elixhauser_hospital, g = 3) 5223.0 115.266
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 5274.1 170.337
## as.factor(callout_year) 5120.3 20.519
## as.factor(callout_wardid == 1) 5162.0 52.263
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 0.903510
## cut2(oasis, g = 3) < 2.2e-16 ***
## request_tele 0.003736 **
## request_vre 4.419e-05 ***
## request_cdiff 0.004641 **
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.002238 **
## as.factor(callout_wardid == 1) 4.855e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_cdiff"## [1] 0.004641178
| I(HOSP_FREE_DAYS <= 7) | ||||
| Odds Ratio | CI | p | ||
| (Intercept) | 0.06 | 0.04 – 0.09 | <.001 | |
| I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”) | 1.01 | 0.80 – 1.28 | .903 | |
| cut2(oasis, g = 3) | ||||
| [27,34) | 1.35 | 1.10 – 1.65 | .003 | |
| [34,64] | 2.26 | 1.87 – 2.75 | <.001 | |
| request_tele | 0.79 | 0.67 – 0.93 | .004 | |
| request_vre | 1.80 | 1.37 – 2.34 | <.001 | |
| request_cdiff | 1.51 | 1.14 – 1.97 | .003 | |
| cut2(elixhauser_hospital, g = 3) | ||||
| [ 1, 7) | 1.67 | 1.35 – 2.08 | <.001 | |
| [ 7,31] | 2.80 | 2.30 – 3.42 | <.001 | |
| cut2(los_pre_callout_days, c(1, 3, 7, 28)) | ||||
| [ 1.000, 3.000) | 1.01 | 0.82 – 1.26 | .928 | |
| [ 3.000, 7.000) | 1.81 | 1.45 – 2.28 | <.001 | |
| [ 7.000, 28.000) | 2.96 | 2.35 – 3.75 | <.001 | |
| [ 28.000,130.762] | 11.26 | 6.23 – 20.37 | <.001 | |
| as.factor(callout_year) | ||||
| 2006 | 0.95 | 0.65 – 1.43 | .813 | |
| 2007 | 0.80 | 0.55 – 1.20 | .270 | |
| 2008 | 0.68 | 0.46 – 1.01 | .049 | |
| 2009 | 0.65 | 0.45 – 0.97 | .031 | |
| 2010 | 0.73 | 0.50 – 1.09 | .112 | |
| 2011 | 0.54 | 0.37 – 0.81 | .003 | |
| as.factor(callout_wardid == 1) (TRUE) | 0.50 | 0.42 – 0.60 | <.001 | |
| Observations | 9673 | |||
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 1.015
## cut2(oasis, g = 3)[27,34) 1.347
## cut2(oasis, g = 3)[34,64] 2.264
## request_tele 0.790
## request_vre 1.798
## request_cdiff 1.507
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 1.674
## cut2(elixhauser_hospital, g = 3)[ 7,31] 2.796
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 1.010
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 1.814
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 2.965
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 11.258
## as.factor(callout_year)2006 0.954
## as.factor(callout_year)2007 0.805
## as.factor(callout_year)2008 0.676
## as.factor(callout_year)2009 0.653
## as.factor(callout_year)2010 0.732
## as.factor(callout_year)2011 0.544
## as.factor(callout_wardid == 1)TRUE 0.501
## 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.799
## cut2(oasis, g = 3)[27,34) 1.104
## cut2(oasis, g = 3)[34,64] 1.867
## request_tele 0.672
## request_vre 1.367
## request_cdiff 1.138
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 1.348
## cut2(elixhauser_hospital, g = 3)[ 7,31] 2.296
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.815
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 1.448
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 2.347
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 6.231
## as.factor(callout_year)2006 0.647
## as.factor(callout_year)2007 0.551
## as.factor(callout_year)2008 0.462
## as.factor(callout_year)2009 0.446
## as.factor(callout_year)2010 0.503
## as.factor(callout_year)2011 0.369
## as.factor(callout_wardid == 1)TRUE 0.419
## 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 1.278
## cut2(oasis, g = 3)[27,34) 1.646
## cut2(oasis, g = 3)[34,64] 2.752
## request_tele 0.927
## request_vre 2.340
## request_cdiff 1.972
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 2.083
## cut2(elixhauser_hospital, g = 3)[ 7,31] 3.423
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 1.256
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 2.278
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 3.754
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 20.367
## as.factor(callout_year)2006 1.429
## as.factor(callout_year)2007 1.196
## as.factor(callout_year)2008 1.007
## as.factor(callout_year)2009 0.972
## as.factor(callout_year)2010 1.086
## as.factor(callout_year)2011 0.815
## as.factor(callout_wardid == 1)TRUE 0.601
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.903
## cut2(oasis, g = 3)[27,34) 0.003
## cut2(oasis, g = 3)[34,64] 0.000
## request_tele 0.004
## request_vre 0.000
## request_cdiff 0.003
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.000
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.928
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,130.762] 0.000
## as.factor(callout_year)2006 0.813
## as.factor(callout_year)2007 0.270
## as.factor(callout_year)2008 0.049
## as.factor(callout_year)2009 0.031
## as.factor(callout_year)2010 0.112
## as.factor(callout_year)2011 0.003
## as.factor(callout_wardid == 1)TRUE 0.000Answer 5: After adjusting for potential confounders, there is no statistically significant evidence that a long delay yield lower rates of poor HFD outcome (<7 days).
Question 6: Do survivors of the hospital stay who have long discharge delays have longer log(LOS_post_icu)?
Post ICU days were similar in the above table 5.95 vs 6.19 (p=0.35)
## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.748
## cut2(oasis, g = 3) 2 8.292
## cut2(age, g = 3) 2 9.628
## female 1 3.869
## request_tele 1 4.450
## request_resp 1 2.611
## request_mrsa 1 14.776
## request_vre 1 8.546
## request_cdiff 1 6.112
## cut2(elixhauser_hospital, g = 3) 2 71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 245.095
## as.factor(callout_month) 11 5.600
## as.factor(callout_year) 6 4.835
## as.factor(callout_dayofweek) 6 14.780
## MED_SERVICE 1 2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 0.126
## RSS
## <none> 4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4466.3
## cut2(oasis, g = 3) 4459.9
## cut2(age, g = 3) 4461.2
## female 4455.4
## request_tele 4456.0
## request_resp 4454.2
## request_mrsa 4466.3
## request_vre 4460.1
## request_cdiff 4457.7
## cut2(elixhauser_hospital, g = 3) 4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4696.7
## as.factor(callout_month) 4457.2
## as.factor(callout_year) 4456.4
## as.factor(callout_dayofweek) 4466.3
## MED_SERVICE 4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
## AIC
## <none> -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6447.1
## cut2(oasis, g = 3) -6462.3
## cut2(age, g = 3) -6459.6
## female -6469.4
## request_tele -6468.2
## request_resp -6472.0
## request_mrsa -6447.1
## request_vre -6459.8
## request_cdiff -6464.8
## cut2(elixhauser_hospital, g = 3) -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -5993.5
## as.factor(callout_month) -6485.9
## as.factor(callout_year) -6477.4
## as.factor(callout_dayofweek) -6457.1
## MED_SERVICE -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1
## F value
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 30.1152
## cut2(oasis, g = 3) 8.4661
## cut2(age, g = 3) 9.8302
## female 7.9010
## request_tele 9.0862
## request_resp 5.3319
## request_mrsa 30.1732
## request_vre 17.4513
## request_cdiff 12.4797
## cut2(elixhauser_hospital, g = 3) 73.1227
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 125.1194
## as.factor(callout_month) 1.0396
## as.factor(callout_year) 1.6456
## as.factor(callout_dayofweek) 5.0301
## MED_SERVICE 5.6742
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 2.6685
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.1289
## Pr(>F)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4.181e-08
## cut2(oasis, g = 3) 0.0002121
## cut2(age, g = 3) 5.438e-05
## female 0.0049513
## request_tele 0.0025825
## request_resp 0.0209614
## request_mrsa 4.058e-08
## request_vre 2.975e-05
## request_cdiff 0.0004134
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4076231
## as.factor(callout_year) 0.1302292
## as.factor(callout_dayofweek) 3.701e-05
## MED_SERVICE 0.0172370
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0459630
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.8790819
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") ***
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) ***
## female **
## request_tele **
## request_resp *
## request_mrsa ***
## request_vre ***
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year)
## as.factor(callout_dayofweek) ***
## MED_SERVICE *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") *
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(age, g = 3) + request_mrsa +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_wardid ==
## 1)
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 16.573
## cut2(age, g = 3) 2 14.802
## request_mrsa 1 13.941
## request_vre 1 9.047
## request_cdiff 1 7.102
## cut2(elixhauser_hospital, g = 3) 2 72.721
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 258.909
## as.factor(callout_wardid == 1) 1 41.927
## RSS AIC
## <none> 4510.1 -6428.0
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4526.7 -6396.5
## cut2(age, g = 3) 4524.9 -6402.0
## request_mrsa 4524.0 -6401.8
## request_vre 4519.1 -6411.7
## request_cdiff 4517.2 -6415.6
## cut2(elixhauser_hospital, g = 3) 4582.8 -6285.8
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4769.0 -5925.8
## as.factor(callout_wardid == 1) 4552.0 -6345.4
## F value
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 33.534
## cut2(age, g = 3) 14.976
## request_mrsa 28.208
## request_vre 18.306
## request_cdiff 14.371
## cut2(elixhauser_hospital, g = 3) 73.574
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 130.973
## as.factor(callout_wardid == 1) 84.837
## Pr(>F)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 7.234e-09 ***
## cut2(age, g = 3) 3.212e-07 ***
## request_mrsa 1.115e-07 ***
## request_vre 1.901e-05 ***
## request_cdiff 0.0001511 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.596
## cut2(oasis, g = 3) 2 8.081
## cut2(age, g = 3) 2 9.483
## female 1 4.071
## request_tele 1 4.283
## request_resp 1 2.489
## request_mrsa 1 14.469
## request_vre 1 8.732
## request_cdiff 1 6.378
## cut2(elixhauser_hospital, g = 3) 2 71.836
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 248.341
## as.factor(callout_dayofweek) 6 15.868
## as.factor(callout_wardid == 1) 1 35.367
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3.827
## MED_SERVICE 1 2.477
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.551
## RSS
## <none> 4462.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4476.8
## cut2(oasis, g = 3) 4470.3
## cut2(age, g = 3) 4471.7
## female 4466.3
## request_tele 4466.5
## request_resp 4464.7
## request_mrsa 4476.7
## request_vre 4471.0
## request_cdiff 4468.6
## cut2(elixhauser_hospital, g = 3) 4534.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4710.6
## as.factor(callout_dayofweek) 4478.1
## as.factor(callout_wardid == 1) 4497.6
## cut2(PROPFULL_BEDS, c(0.9, 1)) 4466.0
## MED_SERVICE 4464.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4465.8
## AIC
## <none> -6491.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6463.7
## cut2(oasis, g = 3) -6479.0
## cut2(age, g = 3) -6476.1
## female -6485.2
## request_tele -6484.7
## request_resp -6488.4
## request_mrsa -6463.9
## request_vre -6475.6
## request_cdiff -6480.5
## cut2(elixhauser_hospital, g = 3) -6349.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -6004.5
## as.factor(callout_dayofweek) -6471.1
## as.factor(callout_wardid == 1) -6421.4
## cut2(PROPFULL_BEDS, c(0.9, 1)) -6487.7
## MED_SERVICE -6488.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6490.2
## F value
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 29.7952
## cut2(oasis, g = 3) 8.2484
## cut2(age, g = 3) 9.6789
## female 8.3101
## request_tele 8.7426
## request_resp 5.0808
## request_mrsa 29.5358
## request_vre 17.8246
## request_cdiff 13.0199
## cut2(elixhauser_hospital, g = 3) 73.3220
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 126.7384
## as.factor(callout_dayofweek) 5.3987
## as.factor(callout_wardid == 1) 72.1964
## cut2(PROPFULL_BEDS, c(0.9, 1)) 3.9061
## MED_SERVICE 5.0557
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 2.4160
## Pr(>F)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4.928e-08 ***
## cut2(oasis, g = 3) 0.0002636 ***
## cut2(age, g = 3) 6.324e-05 ***
## female 0.0039517 **
## request_tele 0.0031166 **
## request_resp 0.0242159 *
## request_mrsa 5.631e-08 ***
## request_vre 2.446e-05 ***
## request_cdiff 0.0003098 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_dayofweek) 1.403e-05 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0201523 *
## MED_SERVICE 0.0245682 *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0644688 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS,
## c(24)) == "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age,
## g = 3) + female + request_tele + request_resp + request_mrsa +
## request_vre + request_cdiff + cut2(elixhauser_hospital, g = 3) +
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) + as.factor(callout_month) +
## as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) * cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7,
## 12, 19)), "[ 7.00,12.00)"), data = (d %>% filter(hospitaldeath ==
## 0)))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6645 -0.4599 -0.0669 0.4172 3.1951
##
## Coefficients:
## Estimate
## (Intercept) 1.489586
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.139340
## cut2(oasis, g = 3)[27,33) 0.054026
## cut2(oasis, g = 3)[33,62] 0.075975
## cut2(age, g = 3)[55.8,73.4) 0.080937
## cut2(age, g = 3)[73.4,91.4] 0.034401
## female 0.041595
## request_tele 0.046959
## request_resp 0.132692
## request_mrsa 0.122078
## request_vre 0.146393
## request_cdiff 0.123316
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.109199
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.225272
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.087810
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.283111
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.543639
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.738618
## as.factor(callout_month)2 -0.009112
## as.factor(callout_month)3 -0.062380
## as.factor(callout_month)4 -0.006644
## as.factor(callout_month)5 0.009055
## as.factor(callout_month)6 -0.038348
## as.factor(callout_month)7 -0.034477
## as.factor(callout_month)8 -0.056856
## as.factor(callout_month)9 -0.025600
## as.factor(callout_month)10 -0.056898
## as.factor(callout_month)11 -0.065643
## as.factor(callout_month)12 -0.053432
## as.factor(callout_year)2006 0.067230
## as.factor(callout_year)2007 0.057870
## as.factor(callout_year)2008 0.056158
## as.factor(callout_year)2009 -0.004633
## as.factor(callout_year)2010 0.040462
## as.factor(callout_year)2011 0.031787
## as.factor(callout_dayofweek)monday -0.112845
## as.factor(callout_dayofweek)saturday -0.037197
## as.factor(callout_dayofweek)sunday -0.073449
## as.factor(callout_dayofweek)thursday -0.031553
## as.factor(callout_dayofweek)tuesday -0.120120
## as.factor(callout_dayofweek)wednesday -0.055300
## as.factor(callout_wardid == 1)TRUE -0.185955
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.021342
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.067086
## MED_SERVICETRUE -0.070920
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00) 0.084643
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00) 0.042077
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87] 0.032486
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.003363
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.026629
## Std. Error
## (Intercept) 0.068191
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.025391
## cut2(oasis, g = 3)[27,33) 0.018359
## cut2(oasis, g = 3)[33,62] 0.019147
## cut2(age, g = 3)[55.8,73.4) 0.018476
## cut2(age, g = 3)[73.4,91.4] 0.019800
## female 0.014798
## request_tele 0.015579
## request_resp 0.057465
## request_mrsa 0.022224
## request_vre 0.035043
## request_cdiff 0.034907
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.018088
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.018637
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.018030
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.022451
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.027327
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.121824
## as.factor(callout_month)2 0.037111
## as.factor(callout_month)3 0.037385
## as.factor(callout_month)4 0.037362
## as.factor(callout_month)5 0.036480
## as.factor(callout_month)6 0.037074
## as.factor(callout_month)7 0.036642
## as.factor(callout_month)8 0.035831
## as.factor(callout_month)9 0.036022
## as.factor(callout_month)10 0.035486
## as.factor(callout_month)11 0.036287
## as.factor(callout_month)12 0.035813
## as.factor(callout_year)2006 0.044610
## as.factor(callout_year)2007 0.043576
## as.factor(callout_year)2008 0.042974
## as.factor(callout_year)2009 0.043142
## as.factor(callout_year)2010 0.042956
## as.factor(callout_year)2011 0.042742
## as.factor(callout_dayofweek)monday 0.027696
## as.factor(callout_dayofweek)saturday 0.029790
## as.factor(callout_dayofweek)sunday 0.029940
## as.factor(callout_dayofweek)thursday 0.027665
## as.factor(callout_dayofweek)tuesday 0.027675
## as.factor(callout_dayofweek)wednesday 0.027301
## as.factor(callout_wardid == 1)TRUE 0.033790
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.044248
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.059726
## MED_SERVICETRUE 0.029773
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00) 0.094514
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00) 0.015504
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87] 0.050599
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.045804
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.061602
## t value
## (Intercept) 21.844
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -5.488
## cut2(oasis, g = 3)[27,33) 2.943
## cut2(oasis, g = 3)[33,62] 3.968
## cut2(age, g = 3)[55.8,73.4) 4.381
## cut2(age, g = 3)[73.4,91.4] 1.737
## female 2.811
## request_tele 3.014
## request_resp 2.309
## request_mrsa 5.493
## request_vre 4.177
## request_cdiff 3.533
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 6.037
## cut2(elixhauser_hospital, g = 3)[ 7,31] 12.087
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 4.870
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 12.610
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 19.894
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 6.063
## as.factor(callout_month)2 -0.246
## as.factor(callout_month)3 -1.669
## as.factor(callout_month)4 -0.178
## as.factor(callout_month)5 0.248
## as.factor(callout_month)6 -1.034
## as.factor(callout_month)7 -0.941
## as.factor(callout_month)8 -1.587
## as.factor(callout_month)9 -0.711
## as.factor(callout_month)10 -1.603
## as.factor(callout_month)11 -1.809
## as.factor(callout_month)12 -1.492
## as.factor(callout_year)2006 1.507
## as.factor(callout_year)2007 1.328
## as.factor(callout_year)2008 1.307
## as.factor(callout_year)2009 -0.107
## as.factor(callout_year)2010 0.942
## as.factor(callout_year)2011 0.744
## as.factor(callout_dayofweek)monday -4.074
## as.factor(callout_dayofweek)saturday -1.249
## as.factor(callout_dayofweek)sunday -2.453
## as.factor(callout_dayofweek)thursday -1.141
## as.factor(callout_dayofweek)tuesday -4.340
## as.factor(callout_dayofweek)wednesday -2.026
## as.factor(callout_wardid == 1)TRUE -5.503
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.482
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -1.123
## MED_SERVICETRUE -2.382
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00) 0.896
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00) 2.714
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87] 0.642
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.073
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.432
## Pr(>|t|)
## (Intercept) < 2e-16
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 4.18e-08
## cut2(oasis, g = 3)[27,33) 0.003262
## cut2(oasis, g = 3)[33,62] 7.31e-05
## cut2(age, g = 3)[55.8,73.4) 1.20e-05
## cut2(age, g = 3)[73.4,91.4] 0.082354
## female 0.004951
## request_tele 0.002583
## request_resp 0.020961
## request_mrsa 4.06e-08
## request_vre 2.98e-05
## request_cdiff 0.000413
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 1.63e-09
## cut2(elixhauser_hospital, g = 3)[ 7,31] < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 1.13e-06
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) < 2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 1.39e-09
## as.factor(callout_month)2 0.806043
## as.factor(callout_month)3 0.095231
## as.factor(callout_month)4 0.858860
## as.factor(callout_month)5 0.803979
## as.factor(callout_month)6 0.300984
## as.factor(callout_month)7 0.346773
## as.factor(callout_month)8 0.112597
## as.factor(callout_month)9 0.477303
## as.factor(callout_month)10 0.108881
## as.factor(callout_month)11 0.070488
## as.factor(callout_month)12 0.135741
## as.factor(callout_year)2006 0.131830
## as.factor(callout_year)2007 0.184203
## as.factor(callout_year)2008 0.191316
## as.factor(callout_year)2009 0.914489
## as.factor(callout_year)2010 0.346241
## as.factor(callout_year)2011 0.457083
## as.factor(callout_dayofweek)monday 4.65e-05
## as.factor(callout_dayofweek)saturday 0.211824
## as.factor(callout_dayofweek)sunday 0.014177
## as.factor(callout_dayofweek)thursday 0.254088
## as.factor(callout_dayofweek)tuesday 1.44e-05
## as.factor(callout_dayofweek)wednesday 0.042835
## as.factor(callout_wardid == 1)TRUE 3.83e-08
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.629580
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.261371
## MED_SERVICETRUE 0.017237
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00) 0.370510
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00) 0.006660
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87] 0.520873
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.941479
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.665558
##
## (Intercept) ***
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE ***
## cut2(oasis, g = 3)[27,33) **
## cut2(oasis, g = 3)[33,62] ***
## cut2(age, g = 3)[55.8,73.4) ***
## cut2(age, g = 3)[73.4,91.4] .
## female **
## request_tele **
## request_resp *
## request_mrsa ***
## request_vre ***
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3)[ 1, 7) ***
## cut2(elixhauser_hospital, g = 3)[ 7,31] ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] ***
## as.factor(callout_month)2
## as.factor(callout_month)3 .
## as.factor(callout_month)4
## as.factor(callout_month)5
## as.factor(callout_month)6
## as.factor(callout_month)7
## as.factor(callout_month)8
## as.factor(callout_month)9
## as.factor(callout_month)10
## as.factor(callout_month)11 .
## as.factor(callout_month)12
## as.factor(callout_year)2006
## as.factor(callout_year)2007
## as.factor(callout_year)2008
## as.factor(callout_year)2009
## as.factor(callout_year)2010
## as.factor(callout_year)2011
## as.factor(callout_dayofweek)monday ***
## as.factor(callout_dayofweek)saturday
## as.factor(callout_dayofweek)sunday *
## as.factor(callout_dayofweek)thursday
## as.factor(callout_dayofweek)tuesday ***
## as.factor(callout_dayofweek)wednesday *
## as.factor(callout_wardid == 1)TRUE ***
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## MED_SERVICETRUE *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[ 0.45, 7.00)
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[12.00,19.00) **
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)")[19.00,23.87]
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000)
## as.factor(callout_wardid == 1)TRUE:cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093]
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6998 on 9090 degrees of freedom
## Multiple R-squared: 0.1225, Adjusted R-squared: 0.1178
## F-statistic: 25.9 on 49 and 9090 DF, p-value: < 2.2e-16## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.748
## cut2(oasis, g = 3) 2 8.292
## cut2(age, g = 3) 2 9.628
## female 1 3.869
## request_tele 1 4.450
## request_resp 1 2.611
## request_mrsa 1 14.776
## request_vre 1 8.546
## request_cdiff 1 6.112
## cut2(elixhauser_hospital, g = 3) 2 71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 245.095
## as.factor(callout_month) 11 5.600
## as.factor(callout_year) 6 4.835
## as.factor(callout_dayofweek) 6 14.780
## MED_SERVICE 1 2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 0.126
## RSS
## <none> 4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4466.3
## cut2(oasis, g = 3) 4459.9
## cut2(age, g = 3) 4461.2
## female 4455.4
## request_tele 4456.0
## request_resp 4454.2
## request_mrsa 4466.3
## request_vre 4460.1
## request_cdiff 4457.7
## cut2(elixhauser_hospital, g = 3) 4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4696.7
## as.factor(callout_month) 4457.2
## as.factor(callout_year) 4456.4
## as.factor(callout_dayofweek) 4466.3
## MED_SERVICE 4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
## AIC
## <none> -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6447.1
## cut2(oasis, g = 3) -6462.3
## cut2(age, g = 3) -6459.6
## female -6469.4
## request_tele -6468.2
## request_resp -6472.0
## request_mrsa -6447.1
## request_vre -6459.8
## request_cdiff -6464.8
## cut2(elixhauser_hospital, g = 3) -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -5993.5
## as.factor(callout_month) -6485.9
## as.factor(callout_year) -6477.4
## as.factor(callout_dayofweek) -6457.1
## MED_SERVICE -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) * cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.748
## cut2(oasis, g = 3) 2 8.292
## cut2(age, g = 3) 2 9.628
## female 1 3.869
## request_tele 1 4.450
## request_resp 1 2.611
## request_mrsa 1 14.776
## request_vre 1 8.546
## request_cdiff 1 6.112
## cut2(elixhauser_hospital, g = 3) 2 71.619
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 245.095
## as.factor(callout_month) 11 5.600
## as.factor(callout_year) 6 4.835
## as.factor(callout_dayofweek) 6 14.780
## MED_SERVICE 1 2.779
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.920
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 2 0.126
## RSS
## <none> 4451.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4466.3
## cut2(oasis, g = 3) 4459.9
## cut2(age, g = 3) 4461.2
## female 4455.4
## request_tele 4456.0
## request_resp 4454.2
## request_mrsa 4466.3
## request_vre 4460.1
## request_cdiff 4457.7
## cut2(elixhauser_hospital, g = 3) 4523.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4696.7
## as.factor(callout_month) 4457.2
## as.factor(callout_year) 4456.4
## as.factor(callout_dayofweek) 4466.3
## MED_SERVICE 4454.3
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4455.5
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 4451.7
## AIC
## <none> -6475.4
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6447.1
## cut2(oasis, g = 3) -6462.3
## cut2(age, g = 3) -6459.6
## female -6469.4
## request_tele -6468.2
## request_resp -6472.0
## request_mrsa -6447.1
## request_vre -6459.8
## request_cdiff -6464.8
## cut2(elixhauser_hospital, g = 3) -6333.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -5993.5
## as.factor(callout_month) -6485.9
## as.factor(callout_year) -6477.4
## as.factor(callout_dayofweek) -6457.1
## MED_SERVICE -6471.6
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6473.3
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) -6479.1
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3.836e-08
## cut2(oasis, g = 3) 0.0002025
## cut2(age, g = 3) 5.152e-05
## female 0.0048326
## request_tele 0.0025123
## request_resp 0.0206083
## request_mrsa 3.722e-08
## request_vre 2.827e-05
## request_cdiff 0.0003984
## cut2(elixhauser_hospital, g = 3) < 2.2e-16
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16
## as.factor(callout_month) 0.4030458
## as.factor(callout_year) 0.1279656
## as.factor(callout_dayofweek) 3.452e-05
## MED_SERVICE 0.0169302
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0450719
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1)) 0.8784589
##
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") ***
## cut2(oasis, g = 3) ***
## cut2(age, g = 3) ***
## female **
## request_tele **
## request_resp *
## request_mrsa ***
## request_vre ***
## request_cdiff ***
## cut2(elixhauser_hospital, g = 3) ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) ***
## as.factor(callout_month)
## as.factor(callout_year)
## as.factor(callout_dayofweek) ***
## MED_SERVICE *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") *
## as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_wardid == 1):cut2(PROPFULL_BEDS, c(0.9, 1))"## [1] 0.8784589## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_month) + as.factor(callout_year) +
## as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.847
## cut2(oasis, g = 3) 2 8.252
## cut2(age, g = 3) 2 9.586
## female 1 3.885
## request_tele 1 4.437
## request_resp 1 2.616
## request_mrsa 1 14.765
## request_vre 1 8.581
## request_cdiff 1 6.167
## cut2(elixhauser_hospital, g = 3) 2 71.774
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 245.249
## as.factor(callout_month) 11 5.586
## as.factor(callout_year) 6 4.865
## as.factor(callout_dayofweek) 6 14.772
## as.factor(callout_wardid == 1) 1 36.056
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5.588
## MED_SERVICE 1 2.793
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.942
## RSS
## <none> 4451.7
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4466.5
## cut2(oasis, g = 3) 4459.9
## cut2(age, g = 3) 4461.3
## female 4455.6
## request_tele 4456.1
## request_resp 4454.3
## request_mrsa 4466.5
## request_vre 4460.3
## request_cdiff 4457.9
## cut2(elixhauser_hospital, g = 3) 4523.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4696.9
## as.factor(callout_month) 4457.3
## as.factor(callout_year) 4456.6
## as.factor(callout_dayofweek) 4466.5
## as.factor(callout_wardid == 1) 4487.8
## cut2(PROPFULL_BEDS, c(0.9, 1)) 4457.3
## MED_SERVICE 4454.5
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4455.6
## AIC
## <none> -6479.1
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6450.7
## cut2(oasis, g = 3) -6466.2
## cut2(age, g = 3) -6463.4
## female -6473.1
## request_tele -6472.0
## request_resp -6475.7
## request_mrsa -6450.8
## request_vre -6463.5
## request_cdiff -6468.4
## cut2(elixhauser_hospital, g = 3) -6336.9
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -5996.9
## as.factor(callout_month) -6489.6
## as.factor(callout_year) -6481.1
## as.factor(callout_dayofweek) -6460.8
## as.factor(callout_wardid == 1) -6407.4
## cut2(PROPFULL_BEDS, c(0.9, 1)) -6471.6
## MED_SERVICE -6475.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6477.0
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 3.459e-08 ***
## cut2(oasis, g = 3) 0.0002110 ***
## cut2(age, g = 3) 5.379e-05 ***
## female 0.0047483 **
## request_tele 0.0025488 **
## request_resp 0.0204856 *
## request_mrsa 3.769e-08 ***
## request_vre 2.725e-05 ***
## request_cdiff 0.0003748 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_month) 0.4053529
## as.factor(callout_year) 0.1253989
## as.factor(callout_dayofweek) 3.479e-05 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0032368 **
## MED_SERVICE 0.0166471 *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0441913 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_month)"## [1] 0.4053529## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_year) + as.factor(callout_dayofweek) +
## as.factor(callout_wardid == 1) + cut2(PROPFULL_BEDS, c(0.9,
## 1)) + MED_SERVICE + relevel(cut2(hourofcallout2, c(7, 12,
## 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 15.090
## cut2(oasis, g = 3) 2 8.310
## cut2(age, g = 3) 2 9.506
## female 1 3.926
## request_tele 1 4.299
## request_resp 1 2.551
## request_mrsa 1 15.020
## request_vre 1 8.678
## request_cdiff 1 6.256
## cut2(elixhauser_hospital, g = 3) 2 72.098
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 244.892
## as.factor(callout_year) 6 4.938
## as.factor(callout_dayofweek) 6 15.319
## as.factor(callout_wardid == 1) 1 35.756
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 5.048
## MED_SERVICE 1 2.838
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.713
## RSS
## <none> 4457.3
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4472.4
## cut2(oasis, g = 3) 4465.6
## cut2(age, g = 3) 4466.8
## female 4461.2
## request_tele 4461.6
## request_resp 4459.8
## request_mrsa 4472.3
## request_vre 4466.0
## request_cdiff 4463.5
## cut2(elixhauser_hospital, g = 3) 4529.4
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4702.2
## as.factor(callout_year) 4462.2
## as.factor(callout_dayofweek) 4472.6
## as.factor(callout_wardid == 1) 4493.0
## cut2(PROPFULL_BEDS, c(0.9, 1)) 4462.3
## MED_SERVICE 4460.1
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4461.0
## AIC
## <none> -6489.6
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6460.7
## cut2(oasis, g = 3) -6476.6
## cut2(age, g = 3) -6474.2
## female -6483.6
## request_tele -6482.8
## request_resp -6486.4
## request_mrsa -6460.9
## request_vre -6473.9
## request_cdiff -6478.8
## cut2(elixhauser_hospital, g = 3) -6347.0
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -6008.8
## as.factor(callout_year) -6491.5
## as.factor(callout_dayofweek) -6470.3
## as.factor(callout_wardid == 1) -6418.6
## cut2(PROPFULL_BEDS, c(0.9, 1)) -6483.3
## MED_SERVICE -6485.8
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6488.0
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 2.728e-08 ***
## cut2(oasis, g = 3) 0.0002010 ***
## cut2(age, g = 3) 5.911e-05 ***
## female 0.0045581 **
## request_tele 0.0029951 **
## request_resp 0.0222027 *
## request_mrsa 2.937e-08 ***
## request_vre 2.482e-05 ***
## request_cdiff 0.0003431 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_year) 0.1197005
## as.factor(callout_dayofweek) 2.164e-05 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0056710 **
## MED_SERVICE 0.0158633 *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0547939 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "as.factor(callout_year)"## [1] 0.1197005## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE + relevel(cut2(hourofcallout2,
## c(7, 12, 19)), "[ 7.00,12.00)")
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.596
## cut2(oasis, g = 3) 2 8.081
## cut2(age, g = 3) 2 9.483
## female 1 4.071
## request_tele 1 4.283
## request_resp 1 2.489
## request_mrsa 1 14.469
## request_vre 1 8.732
## request_cdiff 1 6.378
## cut2(elixhauser_hospital, g = 3) 2 71.836
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 248.341
## as.factor(callout_dayofweek) 6 15.868
## as.factor(callout_wardid == 1) 1 35.367
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3.827
## MED_SERVICE 1 2.477
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 3 3.551
## RSS
## <none> 4462.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4476.8
## cut2(oasis, g = 3) 4470.3
## cut2(age, g = 3) 4471.7
## female 4466.3
## request_tele 4466.5
## request_resp 4464.7
## request_mrsa 4476.7
## request_vre 4471.0
## request_cdiff 4468.6
## cut2(elixhauser_hospital, g = 3) 4534.1
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4710.6
## as.factor(callout_dayofweek) 4478.1
## as.factor(callout_wardid == 1) 4497.6
## cut2(PROPFULL_BEDS, c(0.9, 1)) 4466.0
## MED_SERVICE 4464.7
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 4465.8
## AIC
## <none> -6491.5
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") -6463.7
## cut2(oasis, g = 3) -6479.0
## cut2(age, g = 3) -6476.1
## female -6485.2
## request_tele -6484.7
## request_resp -6488.4
## request_mrsa -6463.9
## request_vre -6475.6
## request_cdiff -6480.5
## cut2(elixhauser_hospital, g = 3) -6349.5
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) -6004.5
## as.factor(callout_dayofweek) -6471.1
## as.factor(callout_wardid == 1) -6421.4
## cut2(PROPFULL_BEDS, c(0.9, 1)) -6487.7
## MED_SERVICE -6488.4
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") -6490.2
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4.673e-08 ***
## cut2(oasis, g = 3) 0.0002563 ***
## cut2(age, g = 3) 6.119e-05 ***
## female 0.0038897 **
## request_tele 0.0030654 **
## request_resp 0.0239719 *
## request_mrsa 5.343e-08 ***
## request_vre 2.368e-05 ***
## request_cdiff 0.0003025 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_dayofweek) 1.341e-05 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0198863 *
## MED_SERVICE 0.0243217 *
## relevel(cut2(hourofcallout2, c(7, 12, 19)), "[ 7.00,12.00)") 0.0637767 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "relevel(cut2(hourofcallout2, c(7, 12, 19)), \"[ 7.00,12.00)\")"## [1] 0.06377669## Single term deletions
##
## Model:
## log(los_post_icu_days + 1) ~ I(cut2(DISCHARGEDELAY_HOURS, c(24)) ==
## "[ 24.000,129.566]") + cut2(oasis, g = 3) + cut2(age, g = 3) +
## female + request_tele + request_resp + request_mrsa + request_vre +
## request_cdiff + cut2(elixhauser_hospital, g = 3) + cut2(los_pre_callout_days,
## c(1, 3, 7, 28)) + as.factor(callout_dayofweek) + as.factor(callout_wardid ==
## 1) + cut2(PROPFULL_BEDS, c(0.9, 1)) + MED_SERVICE
## Df Sum of Sq
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 1 14.130
## cut2(oasis, g = 3) 2 8.072
## cut2(age, g = 3) 2 9.479
## female 1 4.006
## request_tele 1 4.524
## request_resp 1 2.404
## request_mrsa 1 14.427
## request_vre 1 8.755
## request_cdiff 1 6.430
## cut2(elixhauser_hospital, g = 3) 2 72.409
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4 250.962
## as.factor(callout_dayofweek) 6 15.579
## as.factor(callout_wardid == 1) 1 35.176
## cut2(PROPFULL_BEDS, c(0.9, 1)) 2 3.973
## MED_SERVICE 1 2.577
## RSS AIC
## <none> 4465.8 -6490.2
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 4479.9 -6463.4
## cut2(oasis, g = 3) 4473.8 -6477.7
## cut2(age, g = 3) 4475.2 -6474.9
## female 4469.8 -6484.0
## request_tele 4470.3 -6483.0
## request_resp 4468.2 -6487.3
## request_mrsa 4480.2 -6462.8
## request_vre 4474.5 -6474.3
## request_cdiff 4472.2 -6479.1
## cut2(elixhauser_hospital, g = 3) 4538.2 -6347.2
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) 4716.7 -5998.5
## as.factor(callout_dayofweek) 4481.3 -6470.4
## as.factor(callout_wardid == 1) 4500.9 -6420.5
## cut2(PROPFULL_BEDS, c(0.9, 1)) 4469.7 -6486.1
## MED_SERVICE 4468.3 -6487.0
## Pr(>Chi)
## <none>
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]") 7.724e-08 ***
## cut2(oasis, g = 3) 0.0002606 ***
## cut2(age, g = 3) 6.187e-05 ***
## female 0.0042022 **
## request_tele 0.0023487 **
## request_resp 0.0265649 *
## request_mrsa 5.648e-08 ***
## request_vre 2.326e-05 ***
## request_cdiff 0.0002875 ***
## cut2(elixhauser_hospital, g = 3) < 2.2e-16 ***
## cut2(los_pre_callout_days, c(1, 3, 7, 28)) < 2.2e-16 ***
## as.factor(callout_dayofweek) 1.759e-05 ***
## as.factor(callout_wardid == 1) < 2.2e-16 ***
## cut2(PROPFULL_BEDS, c(0.9, 1)) 0.0171892 *
## MED_SERVICE 0.0216504 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] "request_resp"## [1] 0.02656487
| log(los_post_icu_days + 1) | ||||
| Estimate | CI | p | ||
| (Intercept) | 4.43 | 4.08 – 4.82 | <.001 | |
| I(cut2(DISCHARGEDELAY_HOURS, c(24)) == “[ 24.000,129.566]”) | 0.88 | 0.84 – 0.92 | <.001 | |
| cut2(oasis, g = 3) | ||||
| [27,33) | 1.06 | 1.02 – 1.09 | .003 | |
| [33,62] | 1.08 | 1.04 – 1.12 | <.001 | |
| cut2(age, g = 3) | ||||
| [55.8,73.4) | 1.08 | 1.05 – 1.12 | <.001 | |
| [73.4,91.4] | 1.04 | 1.00 – 1.08 | .076 | |
| female | 1.04 | 1.01 – 1.07 | .004 | |
| request_tele | 1.05 | 1.02 – 1.08 | .002 | |
| request_resp | 1.14 | 1.01 – 1.27 | .027 | |
| request_mrsa | 1.13 | 1.08 – 1.18 | <.001 | |
| request_vre | 1.16 | 1.08 – 1.24 | <.001 | |
| request_cdiff | 1.13 | 1.06 – 1.21 | <.001 | |
| cut2(elixhauser_hospital, g = 3) | ||||
| [ 1, 7) | 1.12 | 1.08 – 1.16 | <.001 | |
| [ 7,31] | 1.25 | 1.21 – 1.30 | <.001 | |
| cut2(los_pre_callout_days, c(1, 3, 7, 28)) | ||||
| [ 1.000, 3.000) | 1.10 | 1.06 – 1.14 | <.001 | |
| [ 3.000, 7.000) | 1.34 | 1.28 – 1.40 | <.001 | |
| [ 7.000, 28.000) | 1.73 | 1.64 – 1.83 | <.001 | |
| [ 28.000,110.022] | 2.10 | 1.65 – 2.66 | <.001 | |
| as.factor(callout_dayofweek) | ||||
| monday | 0.89 | 0.84 – 0.94 | <.001 | |
| saturday | 0.97 | 0.92 – 1.03 | .334 | |
| sunday | 0.94 | 0.88 – 0.99 | .029 | |
| thursday | 0.96 | 0.91 – 1.02 | .177 | |
| tuesday | 0.88 | 0.84 – 0.93 | <.001 | |
| wednesday | 0.94 | 0.89 – 0.99 | .022 | |
| as.factor(callout_wardid == 1) (TRUE) | 0.83 | 0.80 – 0.87 | <.001 | |
| cut2(PROPFULL_BEDS, c(0.9, 1)) | ||||
| [0.900,1.000) | 1.00 | 0.96 – 1.04 | .963 | |
| [1.000,1.093] | 0.94 | 0.89 – 0.99 | .018 | |
| MED_SERVICE | 0.93 | 0.88 – 0.99 | .022 | |
| Observations | 9140 | |||
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.131
## cut2(oasis, g = 3)[27,33) 0.054
## cut2(oasis, g = 3)[33,62] 0.074
## cut2(age, g = 3)[55.8,73.4) 0.080
## cut2(age, g = 3)[73.4,91.4] 0.035
## female 0.042
## request_tele 0.047
## request_resp 0.127
## request_mrsa 0.120
## request_vre 0.148
## request_cdiff 0.126
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.109
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.226
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.094
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.290
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.549
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.741
## as.factor(callout_dayofweek)monday -0.115
## as.factor(callout_dayofweek)saturday -0.028
## as.factor(callout_dayofweek)sunday -0.065
## as.factor(callout_dayofweek)thursday -0.037
## as.factor(callout_dayofweek)tuesday -0.126
## as.factor(callout_dayofweek)wednesday -0.062
## as.factor(callout_wardid == 1)TRUE -0.185
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.001
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.062
## MED_SERVICETRUE -0.068
## 2.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.179
## cut2(oasis, g = 3)[27,33) 0.018
## cut2(oasis, g = 3)[33,62] 0.037
## cut2(age, g = 3)[55.8,73.4) 0.044
## cut2(age, g = 3)[73.4,91.4] -0.004
## female 0.013
## request_tele 0.017
## request_resp 0.015
## request_mrsa 0.077
## request_vre 0.079
## request_cdiff 0.058
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.074
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.190
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.058
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.247
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.495
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.503
## as.factor(callout_dayofweek)monday -0.169
## as.factor(callout_dayofweek)saturday -0.086
## as.factor(callout_dayofweek)sunday -0.123
## as.factor(callout_dayofweek)thursday -0.091
## as.factor(callout_dayofweek)tuesday -0.180
## as.factor(callout_dayofweek)wednesday -0.115
## as.factor(callout_wardid == 1)TRUE -0.228
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) -0.040
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.113
## MED_SERVICETRUE -0.126
## 97.5 %
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE -0.083
## cut2(oasis, g = 3)[27,33) 0.090
## cut2(oasis, g = 3)[33,62] 0.112
## cut2(age, g = 3)[55.8,73.4) 0.116
## cut2(age, g = 3)[73.4,91.4] 0.074
## female 0.071
## request_tele 0.077
## request_resp 0.239
## request_mrsa 0.164
## request_vre 0.217
## request_cdiff 0.194
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.145
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.262
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.129
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.334
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.602
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.979
## as.factor(callout_dayofweek)monday -0.061
## as.factor(callout_dayofweek)saturday 0.029
## as.factor(callout_dayofweek)sunday -0.007
## as.factor(callout_dayofweek)thursday 0.017
## as.factor(callout_dayofweek)tuesday -0.072
## as.factor(callout_dayofweek)wednesday -0.009
## as.factor(callout_wardid == 1)TRUE -0.142
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.038
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] -0.011
## MED_SERVICETRUE -0.010
##
## I(cut2(DISCHARGEDELAY_HOURS, c(24)) == "[ 24.000,129.566]")TRUE 0.000
## cut2(oasis, g = 3)[27,33) 0.003
## cut2(oasis, g = 3)[33,62] 0.000
## cut2(age, g = 3)[55.8,73.4) 0.000
## cut2(age, g = 3)[73.4,91.4] 0.076
## female 0.004
## request_tele 0.002
## request_resp 0.027
## request_mrsa 0.000
## request_vre 0.000
## request_cdiff 0.000
## cut2(elixhauser_hospital, g = 3)[ 1, 7) 0.000
## cut2(elixhauser_hospital, g = 3)[ 7,31] 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 1.000, 3.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 3.000, 7.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 7.000, 28.000) 0.000
## cut2(los_pre_callout_days, c(1, 3, 7, 28))[ 28.000,110.022] 0.000
## as.factor(callout_dayofweek)monday 0.000
## as.factor(callout_dayofweek)saturday 0.334
## as.factor(callout_dayofweek)sunday 0.029
## as.factor(callout_dayofweek)thursday 0.177
## as.factor(callout_dayofweek)tuesday 0.000
## as.factor(callout_dayofweek)wednesday 0.022
## as.factor(callout_wardid == 1)TRUE 0.000
## cut2(PROPFULL_BEDS, c(0.9, 1))[0.900,1.000) 0.963
## cut2(PROPFULL_BEDS, c(0.9, 1))[1.000,1.093] 0.018
## MED_SERVICETRUE 0.022